LDA_Lemmatizer.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1e94iPuu2rk4gATKIshQdjd-Jq_jR9Pov",
      "authorship_tag": "ABX9TyPVrruCBwIJZcvEyYgLBKK0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hooooooni/google_review_LDA_english/blob/main/LDA_Lemmatizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 웹 크롤링"
      ],
      "metadata": {
        "id": "Dn6w7EeCsbfh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "D8KGAF4FXK6X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "구글 플레이 스토어에서 리뷰 데이터를 가져올 예정입니다."
      ],
      "metadata": {
        "id": "cQqULGC8Wc7T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "crkfgvhe9Pm6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02bcd8d7-baee-4957-8bef-8f99641eae24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -qq google-play-scraper"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "google-play-scraper는 구글 플레이 스토어 (Google Play Store)에서 앱 정보를 스크래핑하고 수집하기 위한 도구를 제공하는 Python 패키지"
      ],
      "metadata": {
        "id": "XGKKZQLuXDHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -qq -U watermark"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RP5Np6Zf9QuU",
        "outputId": "b80a5d06-c574-46a5-d3e2-9d10fab22fdc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%reload_ext watermark\n",
        "%watermark -v -p pandas,matplotlib,seaborn,google_play_scraper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTQG5SYq9TbP",
        "outputId": "974053b5-0b66-405a-f88e-2b83de01ad57"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python implementation: CPython\n",
            "Python version       : 3.10.12\n",
            "IPython version      : 7.34.0\n",
            "\n",
            "pandas             : 2.1.2\n",
            "matplotlib         : 3.7.1\n",
            "seaborn            : 0.12.2\n",
            "google_play_scraper: 1.2.4\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from pygments import highlight\n",
        "from pygments.lexers import JsonLexer\n",
        "from pygments.formatters import TerminalFormatter\n",
        "\n",
        "from google_play_scraper import Sort, reviews, app\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format='retina'\n",
        "\n",
        "sns.set(style='whitegrid', palette='muted', font_scale=1.2)"
      ],
      "metadata": {
        "id": "YN7cPy-u9VUD"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- json: JSON 데이터 처리를 위한 파이썬 표준 라이브러리.\n",
        "- pandas: 데이터 프레임을 다루기 위한 라이브러리.\n",
        "- tqdm: 진행 상황을 시각적으로 표시해주는 라이브러리.\n",
        "- seaborn: Matplotlib을 기반으로한 통계용 그래픽 라이브러리로, 시각화를 보다 멋지게 표현할 수 있도록 도와줌.\n",
        "- matplotlib.pyplot: 데이터 시각화를 위한 라이브러리.\n",
        "- pygments: 코드 문법 강조를 위한 라이브러리.\n",
        "\n",
        "<br><br>\n",
        "\n",
        "- `google_play_scraper`: Google Play 스토어에서 앱 정보 및 리뷰를 가져오기 위한 라이브러리\n",
        "- `%matplotlib inline`: Jupyter Notebook에서 그래픽 출력을 활성화.\n",
        "- `%config InlineBackend.figure_format='retina'`: 그래픽 출력 설정을 'retina'로 지정하여 고해상도 그래픽 출력을 활성화.\n",
        "- `sns.set()`: Seaborn 라이브러리의 스타일, 팔레트, 글꼴 크기 등을 초기화하여 설정."
      ],
      "metadata": {
        "id": "s1v09WoTXXx9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "app_packages = [\n",
        "  'com.anydo',\n",
        "  'com.todoist',\n",
        "  'com.ticktick.task',\n",
        "  'com.habitrpg.android.habitica',\n",
        "  'cc.forestapp',\n",
        "  'com.oristats.habitbull',\n",
        "  'com.levor.liferpgtasks',\n",
        "  'com.habitnow',\n",
        "  'com.microsoft.todos',\n",
        "  'prox.lab.calclock',\n",
        "  'com.gmail.jmartindev.timetune',\n",
        "  'com.artfulagenda.app',\n",
        "  'com.tasks.android',\n",
        "  'com.appgenix.bizcal',\n",
        "  'com.appxy.planner'\n",
        "]"
      ],
      "metadata": {
        "id": "FXQvc53P9aJK"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " * 각 항목은 구글 플레이 스토어에서 특정 앱을 식별하는 데 사용되는 패키지 이름"
      ],
      "metadata": {
        "id": "fJMVM9pIYCA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "app_infos = []\n",
        "\n",
        "for ap in tqdm(app_packages):\n",
        "  info = app(ap, lang='en', country='us')\n",
        "  del info['comments']\n",
        "  app_infos.append(info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dC-ZPOP_9b0c",
        "outputId": "b680d9bb-40a4-4e11-a7e2-0ef1d4789724"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15/15 [00:03<00:00,  3.90it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-> 리뷰 정보를 제외한 앱 정보를 리스트에 추가합니다. <br>\n",
        "lang 매개변수는 언어를 나타내며, 여기서는 영어로 설정되어 있습니다. country 매개변수는 국가를 나타내며, 여기서는 미국으로 설정되어 있습니다."
      ],
      "metadata": {
        "id": "bnfgf26aYNXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def print_json(json_object):\n",
        "  json_str = json.dumps(\n",
        "    json_object,\n",
        "    indent=2,\n",
        "    sort_keys=True,\n",
        "    default=str\n",
        "  )\n",
        "  print(highlight(json_str, JsonLexer(), TerminalFormatter()))"
      ],
      "metadata": {
        "id": "ksSex9IG9f-3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "print_json이라는 이름의 함수를 정의합니다. 이 함수는 JSON 객체를 인자로 받아서 읽기 좋게 출력하는 역할을 합니다."
      ],
      "metadata": {
        "id": "jz3B5ccMY7_h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "json.dumps 함수를 사용하여 JSON 객체를 문자열로 변환합니다. <br>\n",
        "\n",
        "- json_object: 변환할 JSON 객체를 나타냅니다.\n",
        "- indent=2: 들여쓰기를 2칸으로 설정하여 출력 형식을 보기 좋게 만듭니다.\n",
        "- sort_keys=True: 키를 알파벳 순서로 정렬하여 출력합니다.\n",
        "- default=str: 기본적으로는 JSON 인코딩 중에 발생하는 TypeError를 방지하기 위해 사용되며, str 함수를 사용하여 객체를 문자열로 변환합니다."
      ],
      "metadata": {
        "id": "MvsffU0hY-bN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "- pygments 라이브러리를 사용하여 JSON 문자열을 하이라이트 처리한 후 출력합니다.\n",
        "\n",
        "\n",
        "> JsonLexer(): JSON 형식에 대한 lexer(문법 강조기)를 생성합니다.\n",
        "\n",
        "> TerminalFormatter(): 터미널에 출력할 때 사용할 formatter(서식 지정기)를 생성합니다.\n",
        "\n",
        "> highlight() 함수로 JSON 문자열을 하이라이트 처리하고, print() 함수로 출력합니다.\n",
        "\n"
      ],
      "metadata": {
        "id": "yLsDaMmGZAb2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print_json(app_infos[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "denO8Clj9hp4",
        "outputId": "7d578b92-3f3a-4991-da8f-0ee70b828bb0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"adSupported\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34mfalse\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
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            "\u001b[37m    \u001b[39;49;00m}\u001b[37m\u001b[39;49;00m\n",
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            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"contentRating\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"Everyone\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"contentRatingDescription\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34mnull\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"currency\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"USD\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"description\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"\\ud83e\\udd47 <b>\\\"#1 to do list app out there\\u201d</b> - WSJ\\r\\n\\ud83c\\udfc6 <b>Editor's Choice</b> by Google\\r\\n\\r\\nOver 40M people rely on Any.do to stay organized and get more done.\\r\\nIt's a simple to do list app with reminders, a planner & calendar - all in one.\\r\\n\\r\\n<b>\\ud83e\\udd47 \\\"A MUST HAVE APP\\\" (Lifehacker, NYTimes, USA TODAY).</b>\\r\\n\\r\\nAny.do is a free to-do list, planner & calendar app for managing and organizing your daily tasks, to-do lists, notes, reminders, checklists, calendar events, grocery lists, and more.\\r\\n\\r\\nOrganize your tasks & to-do list\\r\\n\\r\\n\\u2022 ADVANCED CALENDAR & DAILY PLANNER - Keep your to-do list and calendar events always at hand with our calendar widget. Any.do to-do list & planner support daily calendar view, 3-day Calendar view, Weekly calendar view & agenda view, with built-in reminders. Review and organize your calendar events and to do list side by side.\\r\\n\\r\\n\\u2022 SYNCS SEAMLESSLY - Keeps all your to do list, tasks, reminders, notes, calendar & agenda always in sync so you\\u2019ll never forget a thing. Sync your phone\\u2019s calendar, google calendar, Facebook events, outlook calendar, or any other calendar so you don\\u2019t forget an important event. Even on your Wear OS device.\\r\\n\\r\\n\\u2022 SET REMINDERS - One-time reminders, recurring reminders, Location reminders & voice reminders. NEW! Easily create tasks and get reminders in WhatsApp.\\r\\n\\r\\n\\u2022 WORK TOGETHER - Share your to do list and assign tasks with your friends, family & colleagues from your task list to collaborate and get more done. \\r\\n\\r\\n---\\r\\n\\r\\nALL-IN-ONE PLANNER AND CALENDAR APP FOR GETTING THINGS DONE\\r\\nCreate and set reminders with voice to your to do list. \\r\\nFor better task management flow we added a calendar integration to keep your agenda always up to date. \\r\\nFor better productivity, we added recurring reminders, location reminders, one-time reminders, sub-tasks, notes & file attachments. \\r\\nTo keep your to do list up to date, we\\u2019ve added a daily planner and focus mode.\\r\\n\\r\\nINTEGRATIONS\\r\\nAny.do To do list, Calendar, planner & Reminders Integrates with Google Calendar, Outlook, WhatsApp, Slack, Gmail, Google Tasks, Evernote, Trello, Wunderlist, Todoist, Zapier, Asana, Microsoft to-do, Salesforce, OneNote, Google Assistant, Amazon Alexa, Office 365, Exchange, Jira & More.\\r\\n\\r\\nTO DO LIST, CALENDAR, PLANNER & REMINDERS MADE SIMPLE\\r\\nDesigned to keep you on top of your to do list, tasks and calendar events with no hassle. With intuitive drag and drop of tasks, swiping to mark to-do's as complete, and shaking your device to remove completed from your to do list - you can stay organized and enjoy every minute of it.\\r\\n\\r\\nPOWERFUL TO DO LIST TASK MANAGEMENT\\r\\nAdd a to do list item straight from your email / Gmail / Outlook inbox by forwarding do@Any.do. Attach files from your computer, Dropbox, or Google Drive to your to-tasks.\\r\\n\\r\\nDAILY PLANNER AND LIFE ORGANIZER\\r\\nAny.do is a to do list, a calendar, an inbox, a notepad, a checklist, a task list, a board for post its or sticky notes, a task & project management tool, a reminder app, a daily planner, a family organizer, an agenda, a bill planner and overall the simplest productivity tool you will ever have. \\r\\n\\r\\nSHARE LISTS, ASSIGN & ORGANIZE TASKS\\r\\nPlanning & organizing projects has never been easier. Now you can share lists between family members, assign tasks to each other, chat, and much more. Any.do will help you and the people around you stay in sync and get reminders so that you can focus on what matters, knowing you had a productive day and crossed off your to do list.\\r\\n\\r\\nGROCERY LIST AND SHOPPING LIST\\r\\nAny.do task list, calendar, agenda, reminders & planner is also great for shopping lists at the grocery store. Simply create a list on Any.do, share it with your loved ones, and see them adding their shopping items in real time.\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"descriptionHTML\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"\\ud83e\\udd47 <b>&quot;#1 to do list app out there\\u201d</b> - WSJ<br>\\ud83c\\udfc6 <b>Editor&#39;s Choice</b> by Google<br><br>Over 40M people rely on Any.do to stay organized and get more done.<br>It&#39;s a simple to do list app with reminders, a planner &amp; calendar - all in one.<br><br><b>\\ud83e\\udd47 &quot;A MUST HAVE APP&quot; (Lifehacker, NYTimes, USA TODAY).</b><br><br>Any.do is a free to-do list, planner &amp; calendar app for managing and organizing your daily tasks, to-do lists, notes, reminders, checklists, calendar events, grocery lists, and more.<br><br>Organize your tasks &amp; to-do list<br><br>\\u2022 ADVANCED CALENDAR &amp; DAILY PLANNER - Keep your to-do list and calendar events always at hand with our calendar widget. Any.do to-do list &amp; planner support daily calendar view, 3-day Calendar view, Weekly calendar view &amp; agenda view, with built-in reminders. Review and organize your calendar events and to do list side by side.<br><br>\\u2022 SYNCS SEAMLESSLY - Keeps all your to do list, tasks, reminders, notes, calendar &amp; agenda always in sync so you\\u2019ll never forget a thing. Sync your phone\\u2019s calendar, google calendar, Facebook events, outlook calendar, or any other calendar so you don\\u2019t forget an important event. Even on your Wear OS device.<br><br>\\u2022 SET REMINDERS - One-time reminders, recurring reminders, Location reminders &amp; voice reminders. NEW! Easily create tasks and get reminders in WhatsApp.<br><br>\\u2022 WORK TOGETHER - Share your to do list and assign tasks with your friends, family &amp; colleagues from your task list to collaborate and get more done. <br><br>---<br><br>ALL-IN-ONE PLANNER AND CALENDAR APP FOR GETTING THINGS DONE<br>Create and set reminders with voice to your to do list. <br>For better task management flow we added a calendar integration to keep your agenda always up to date. <br>For better productivity, we added recurring reminders, location reminders, one-time reminders, sub-tasks, notes &amp; file attachments. <br>To keep your to do list up to date, we\\u2019ve added a daily planner and focus mode.<br><br>INTEGRATIONS<br>Any.do To do list, Calendar, planner &amp; Reminders Integrates with Google Calendar, Outlook, WhatsApp, Slack, Gmail, Google Tasks, Evernote, Trello, Wunderlist, Todoist, Zapier, Asana, Microsoft to-do, Salesforce, OneNote, Google Assistant, Amazon Alexa, Office 365, Exchange, Jira &amp; More.<br><br>TO DO LIST, CALENDAR, PLANNER &amp; REMINDERS MADE SIMPLE<br>Designed to keep you on top of your to do list, tasks and calendar events with no hassle. With intuitive drag and drop of tasks, swiping to mark to-do&#39;s as complete, and shaking your device to remove completed from your to do list - you can stay organized and enjoy every minute of it.<br><br>POWERFUL TO DO LIST TASK MANAGEMENT<br>Add a to do list item straight from your email / Gmail / Outlook inbox by forwarding do@Any.do. Attach files from your computer, Dropbox, or Google Drive to your to-tasks.<br><br>DAILY PLANNER AND LIFE ORGANIZER<br>Any.do is a to do list, a calendar, an inbox, a notepad, a checklist, a task list, a board for post its or sticky notes, a task &amp; project management tool, a reminder app, a daily planner, a family organizer, an agenda, a bill planner and overall the simplest productivity tool you will ever have. <br><br>SHARE LISTS, ASSIGN &amp; ORGANIZE TASKS<br>Planning &amp; organizing projects has never been easier. Now you can share lists between family members, assign tasks to each other, chat, and much more. Any.do will help you and the people around you stay in sync and get reminders so that you can focus on what matters, knowing you had a productive day and crossed off your to do list.<br><br>GROCERY LIST AND SHOPPING LIST<br>Any.do task list, calendar, agenda, reminders &amp; planner is also great for shopping lists at the grocery store. Simply create a list on Any.do, share it with your loved ones, and see them adding their shopping items in real time.\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
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            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/9OODkBRjO6zKfXmFp_uGcHCWpmyj3Zm_aQudFu0IHnNsGHgsrNMphI9uV_DD_L0DZQ\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/k7Yj3HBEdv0JzK3tUP_qAncV3sRkz8hem0QtQM_hONDwuOxu78K-BajM1LX44te5ag\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/ALOTuOv9d3QtnaICGmA_CeKt7_uDQFzA0puwlh3PioUobMBN7ejtjF9JAZZ7ey69IA\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/SzVAYIAtdrwPvbIDUd8skDwC7QPxnVbR7hh7KHAMLHdU2ffirHB3CTO4bnT0LOyItA\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/pDQUCLKKKLyUoZ7TGffHV6ajRAUQutsWNRZtZ9w8OHj-3vvLSHysYxVV-ESrkzi7wg\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/DWkaFH83ySIHbOTKtWy2nib_1YnfxTWQ6j4UO22Jf5Pr6VKBAYJYMAIxqf1GsZIGj_U\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m    \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/nIs3xSSM9pvXv-kekNw8G5RVEV0GXW7t3aI74MSOZZ4u72Pq3GgrjBN3L-EjrfNl96I\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m],\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"summary\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"Simple planner with to-do list, tasks, reminders, checklists, calendar & agenda\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"title\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"Any.do - To do list & Calendar\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"updated\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34m1697389826\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"url\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"https://play.google.com/store/apps/details?id=com.anydo&hl=en&gl=us\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"version\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"5.17.0.148\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"video\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34mnull\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"videoImage\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34mnull\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
            "}\u001b[37m\u001b[39;49;00m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "가져온 리뷰 정보를 확인합니다."
      ],
      "metadata": {
        "id": "sHujBiM_Ynu-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib\n",
        "import numpy as np\n",
        "from PIL import Image  # Image 클래스를 가져옵니다.\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def format_title(title):\n",
        "  sep_index = title.find(':') if title.find(':') != -1 else title.find('-')\n",
        "  if sep_index != -1:\n",
        "    title = title[:sep_index]\n",
        "  return title[:10]\n",
        "\n",
        "fig, axs = plt.subplots(2, len(app_infos) // 2, figsize=(14, 5))\n",
        "\n",
        "for i, ax in enumerate(axs.flat):\n",
        "    ai = app_infos[i]\n",
        "    img = Image.open(urllib.request.urlopen(ai['icon']))\n",
        "    ax.imshow(np.array(img))\n",
        "    ax.set_title(format_title(ai['title']))\n",
        "    ax.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "O_-DMqOP9lS-",
        "outputId": "e1adbdaa-df02-478a-ddbd-7001ee607a8e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 14 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 1105,
              "height": 386
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "리뷰를 가져온 앱들이 어떤 앱인지 확인할 수 있는 코드입니다."
      ],
      "metadata": {
        "id": "zxWCk6VIYx-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "app_infos_df = pd.DataFrame(app_infos)\n",
        "app_infos_df.to_csv('apps.csv', index=None, header=True)"
      ],
      "metadata": {
        "id": "TCZHlYhC9l4u"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "app_infos 리스트에 저장된 앱 정보를 DataFrame으로 변환하고, 이를 CSV 파일로 저장하는 부분"
      ],
      "metadata": {
        "id": "zin0LKh0bDRC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "app_reviews = []\n",
        "\n",
        "for ap in tqdm(app_packages): #app_packages에 있는 각 앱 패키지에 대해 반복하고,\n",
        "# 각 앱에 대해 1부터 5까지의 점수와 정렬 방법(MOST_RELEVANT, NEWEST)에 대해 반복합니다.\n",
        "  for score in list(range(1, 6)):\n",
        "    for sort_order in [Sort.MOST_RELEVANT, Sort.NEWEST]:\n",
        "      rvs, _ = reviews(\n",
        "        ap,\n",
        "        lang='en',\n",
        "        country='us',\n",
        "        sort=sort_order,\n",
        "        count= 200 if score == 3 else 100,\n",
        "        filter_score_with=score\n",
        "      )\n",
        "      for r in rvs:\n",
        "        r['sortOrder'] = 'most_relevant' if sort_order == Sort.MOST_RELEVANT else 'newest'\n",
        "        r['appId'] = ap\n",
        "      app_reviews.extend(rvs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wExPG369oBL",
        "outputId": "7b0527b6-375b-46da-ced7-b52de9295e9e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 15/15 [00:34<00:00,  2.30s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "google_play_scraper 라이브러리의 reviews 함수를 사용하여 각 앱에 대한 리뷰를 가져옵니다. ap는 현재 앱 패키지, score는 현재 점수, sort_order는 정렬 방법을 나타냅니다. 리뷰는 최대 200개(점수가 3점일 때) 또는 100개(그 외 점수)까지 가져옵니다."
      ],
      "metadata": {
        "id": "FFVFOW45a1xe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print_json(app_reviews[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODj-1fX_9p5_",
        "outputId": "30619422-d0b2-4896-d823-60de32ccf14e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"appId\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"com.anydo\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"appVersion\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"5.17.0.119\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"at\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"2023-07-02 17:35:08\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"content\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"I used to love this app, but recently they did a big push for premium. At the same time, I started having a huge issue with my lists not opening. The screen shows, but it is grayed out and I can't click on anything. I tried the premium to see if that would help, but it hasn't. My lists are inaccessible to me. I have tried uninstalling and reinstalling the app to noavail.\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"repliedAt\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"2023-07-03 09:28:29\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"replyContent\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"You should only see the upgrade ad when you first log into your accout or when you are trying to use premium features. You can always dismiss it, though! As for the lists issue - please contact us at https://www.any.do/contact_form or via Settings \\u2192 Support and we will get that sorted for you! \\ud83d\\ude0a\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"reviewCreatedVersion\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"5.17.0.119\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"reviewId\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"eecc1d6f-2e1b-4d5c-bf06-e2ce6718c410\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"score\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34m1\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"sortOrder\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"most_relevant\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"thumbsUpCount\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[34m162\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"userImage\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"https://play-lh.googleusercontent.com/a/ACg8ocLthhm2HpfgOL2Ul47HhoLrvpyq3Cr9RsTmM9VNmsM-=mo\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
            "\u001b[37m  \u001b[39;49;00m\u001b[94m\"userName\"\u001b[39;49;00m:\u001b[37m \u001b[39;49;00m\u001b[33m\"Krista Clark\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
            "}\u001b[37m\u001b[39;49;00m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(app_reviews)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9kYFrJA9sNp",
        "outputId": "1f29dc9f-1c3f-4baf-8924-a324123282de"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17608"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "리뷰들이 몇 개인지 길이를 확인하는 코드입니다."
      ],
      "metadata": {
        "id": "b2McED9FZkX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "app_reviews_df = pd.DataFrame(app_reviews)\n",
        "app_reviews_df.to_csv('reviews.csv', index=None, header=True)\n",
        "\n",
        "files.download('reviews.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "gCg8PWjx9vEd",
        "outputId": "b04e85e6-6fb9-4187-a026-ef6aa1addc8c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_2d36702f-324c-4f0c-9067-0f887cfedbeb\", \"reviews.csv\", 10420206)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "app_reviews 리스트에 저장된 앱 리뷰 정보를 DataFrame으로 변환하고, to_csv 함수를 사용하여 'reviews.csv'라는 파일로 저장합니다.\n",
        "\n",
        "- 'reviews.csv': 저장할 CSV 파일의 이름을 지정합니다.\n",
        "\n",
        "- index=None: DataFrame의 인덱스를 CSV 파일에 저장하지 않도록 설정합니다.\n",
        "\n",
        "- header=True: 헤더(열 이름)를 CSV 파일에 포함시킵니다."
      ],
      "metadata": {
        "id": "GkaGmRlOaUiN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "files.download 함수를 사용하여 'reviews.csv' 파일을 로컬로 다운로드합니다. 이를 구글 코랩 환경에서 실행할 경우 파일이 로컬로 다운로드됩니다."
      ],
      "metadata": {
        "id": "4s4pvc2uabM4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 가상환경 구축 및 데이터, 패키지 불러오기"
      ],
      "metadata": {
        "id": "uSNd6zdj-fxH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1) 가상환경 구축"
      ],
      "metadata": {
        "id": "i0qGLGv9sro1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "구글 코랩에서 기본적으로 제공하고 있는 패키지의 버전들은 해당 프로젝트를 import 하는 데에 있어 적합하지 않아 코랩에서 제공하는 Conda 가상환경을 구축해야 합니다."
      ],
      "metadata": {
        "id": "4KulDOMwtGQM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsiLz2Ot-hfz",
        "outputId": "b9cf4d3f-2cb8-4e0b-ec09-67ca742ca37a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m✨🍰✨ Everything looks OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import condacolab"
      ],
      "metadata": {
        "id": "JvrXpq8p-ic_"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!conda create --name lda python=3.8"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTlRjXJbtvhE",
        "outputId": "fe444563-826c-493b-9888-3dd1e59e0889"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Solving environment: \\ \b\b| \b\b/ \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.9.0\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.9.0\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local/envs/lda\n",
            "\n",
            "  added / updated specs:\n",
            "    - python=3.8\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2023.7.22  |       hbcca054_0         146 KB  conda-forge\n",
            "    libgcc-ng-13.2.0           |       h807b86a_2         753 KB  conda-forge\n",
            "    libgomp-13.2.0             |       h807b86a_2         411 KB  conda-forge\n",
            "    libnsl-2.0.1               |       hd590300_0          33 KB  conda-forge\n",
            "    libsqlite-3.43.2           |       h2797004_0         820 KB  conda-forge\n",
            "    libzlib-1.2.13             |       hd590300_5          60 KB  conda-forge\n",
            "    ncurses-6.4                |       hcb278e6_0         860 KB  conda-forge\n",
            "    openssl-3.1.4              |       hd590300_0         2.5 MB  conda-forge\n",
            "    pip-23.3.1                 |     pyhd8ed1ab_0         1.3 MB  conda-forge\n",
            "    python-3.8.18              |hd12c33a_0_cpython        23.3 MB  conda-forge\n",
            "    setuptools-68.2.2          |     pyhd8ed1ab_0         454 KB  conda-forge\n",
            "    tk-8.6.13                  |       h2797004_0         3.1 MB  conda-forge\n",
            "    wheel-0.41.2               |     pyhd8ed1ab_0          56 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:        33.8 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge \n",
            "  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-2_gnu \n",
            "  bzip2              conda-forge/linux-64::bzip2-1.0.8-h7f98852_4 \n",
            "  ca-certificates    conda-forge/linux-64::ca-certificates-2023.7.22-hbcca054_0 \n",
            "  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.40-h41732ed_0 \n",
            "  libffi             conda-forge/linux-64::libffi-3.4.2-h7f98852_5 \n",
            "  libgcc-ng          conda-forge/linux-64::libgcc-ng-13.2.0-h807b86a_2 \n",
            "  libgomp            conda-forge/linux-64::libgomp-13.2.0-h807b86a_2 \n",
            "  libnsl             conda-forge/linux-64::libnsl-2.0.1-hd590300_0 \n",
            "  libsqlite          conda-forge/linux-64::libsqlite-3.43.2-h2797004_0 \n",
            "  libuuid            conda-forge/linux-64::libuuid-2.38.1-h0b41bf4_0 \n",
            "  libzlib            conda-forge/linux-64::libzlib-1.2.13-hd590300_5 \n",
            "  ncurses            conda-forge/linux-64::ncurses-6.4-hcb278e6_0 \n",
            "  openssl            conda-forge/linux-64::openssl-3.1.4-hd590300_0 \n",
            "  pip                conda-forge/noarch::pip-23.3.1-pyhd8ed1ab_0 \n",
            "  python             conda-forge/linux-64::python-3.8.18-hd12c33a_0_cpython \n",
            "  readline           conda-forge/linux-64::readline-8.2-h8228510_1 \n",
            "  setuptools         conda-forge/noarch::setuptools-68.2.2-pyhd8ed1ab_0 \n",
            "  tk                 conda-forge/linux-64::tk-8.6.13-h2797004_0 \n",
            "  wheel              conda-forge/noarch::wheel-0.41.2-pyhd8ed1ab_0 \n",
            "  xz                 conda-forge/linux-64::xz-5.2.6-h166bdaf_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "ncurses-6.4          | 860 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "setuptools-68.2.2    | 454 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "ca-certificates-2023 | 146 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 411 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tk-8.6.13            | 3.1 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.1.4        | 2.5 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.43.2     | 820 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnsl-2.0.1         | 33 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 753 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pip-23.3.1           | 1.3 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "wheel-0.41.2         | 56 KB     | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "ncurses-6.4          | 860 KB    | :   2% 0.018597745432008237/1 [00:00<00:08,  8.28s/it]\n",
            "setuptools-68.2.2    | 454 KB    | :   4% 0.03528000706289204/1 [00:00<00:04,  4.80s/it]\u001b[A\n",
            "\n",
            "\n",
            "libgomp-13.2.0       | 411 KB    | :   4% 0.03890457408942068/1 [00:00<00:04,  4.30s/it]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 KB     | :  27% 0.26602584919140093/1 [00:00<00:00,  1.60it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "ca-certificates-2023 | 146 KB    | :  11% 0.10958097849714075/1 [00:00<00:01,  1.57s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tk-8.6.13            | 3.1 MB    | :   0% 0.004979656171518519/1 [00:00<00:42, 43.18s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "libzlib-1.2.13       | 60 KB     | : 100% 1.0/1 [00:00<00:00,  1.60it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libsqlite-3.43.2     | 820 KB    | :   2% 0.01950733966035988/1 [00:00<00:12, 12.63s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.1.4        | 2.5 MB    | :   1% 0.006187297158692239/1 [00:00<00:39, 40.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libnsl-2.0.1         | 33 KB     | :  49% 0.4904214559386973/1 [00:00<00:00,  1.95it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "libgcc-ng-13.2.0     | 753 KB    | :   2% 0.02124665913662105/1 [00:00<00:11, 12.04s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "pip-23.3.1           | 1.3 MB    | :   1% 0.0117125785830811/1 [00:00<00:25, 25.74s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "tk-8.6.13            | 3.1 MB    | :  65% 0.652334958468926/1 [00:00<00:00,  2.59it/s]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "wheel-0.41.2         | 56 KB     | :  28% 0.2849986084052324/1 [00:00<00:00,  1.22s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "openssl-3.1.4        | 2.5 MB    | :  77% 0.7672248476778376/1 [00:00<00:00,  2.77it/s]  \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "python-3.8.18        | 23.3 MB   | :   0% 0.0006703980472038132/1 [00:00<08:46, 527.24s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-3.8.18        | 23.3 MB   | :  10% 0.10055970708057199/1 [00:00<00:03,  3.46s/it]   \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-3.8.18        | 23.3 MB   | :  21% 0.21117538486920118/1 [00:00<00:01,  1.90s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "python-3.8.18        | 23.3 MB   | :  31% 0.3137462860913846/1 [00:00<00:01,  1.49s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "setuptools-68.2.2    | 454 KB    | : 100% 1.0/1 [00:00<00:00,  1.59it/s]                \u001b[A\n",
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            "\n",
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            "\n",
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            "python-3.8.18        | 23.3 MB   | :  44% 0.43709952677688624/1 [00:00<00:00,  1.20s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libgomp-13.2.0       | 411 KB    | : 100% 1.0/1 [00:00<00:00,  1.32it/s]                \u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "libnsl-2.0.1         | 33 KB     | : 100% 1.0/1 [00:00<00:00,  1.11it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libnsl-2.0.1         | 33 KB     | : 100% 1.0/1 [00:00<00:00,  1.11it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "python-3.8.18        | 23.3 MB   | :  54% 0.5390000299518659/1 [00:00<00:00,  1.15s/it] \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "python-3.8.18        | 23.3 MB   | :  67% 0.6690572511094056/1 [00:00<00:00,  1.01s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "libsqlite-3.43.2     | 820 KB    | : 100% 1.0/1 [00:01<00:00,  1.03it/s]                \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "python-3.8.18        | 23.3 MB   | :  80% 0.7957624820309264/1 [00:01<00:00,  1.05it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "wheel-0.41.2         | 56 KB     | : 100% 1.0/1 [00:01<00:00,  1.11s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "wheel-0.41.2         | 56 KB     | : 100% 1.0/1 [00:01<00:00,  1.11s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "python-3.8.18        | 23.3 MB   | :  95% 0.9472724406989882/1 [00:01<00:00,  1.18it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pip-23.3.1           | 1.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.73s/it]               \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "pip-23.3.1           | 1.3 MB    | : 100% 1.0/1 [00:01<00:00,  1.73s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "\n",
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            "ncurses-6.4          | 860 KB    | : 100% 1.0/1 [00:02<00:00,  2.95s/it]\n",
            "\n",
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            "                                                                        \n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
            "#\n",
            "# To activate this environment, use\n",
            "#\n",
            "#     $ conda activate lda\n",
            "#\n",
            "# To deactivate an active environment, use\n",
            "#\n",
            "#     $ conda deactivate\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!conda activate lda"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nc5k3iDvt-yi",
        "outputId": "0c1ffa90-0765-4c34-fab2-6a84fc4c467d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.\n",
            "To initialize your shell, run\n",
            "\n",
            "    $ conda init <SHELL_NAME>\n",
            "\n",
            "Currently supported shells are:\n",
            "  - bash\n",
            "  - fish\n",
            "  - tcsh\n",
            "  - xonsh\n",
            "  - zsh\n",
            "  - powershell\n",
            "\n",
            "See 'conda init --help' for more information and options.\n",
            "\n",
            "IMPORTANT: You may need to close and restart your shell after running 'conda init'.\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!source activate lda"
      ],
      "metadata": {
        "id": "Ge-DG4SUuJ4s"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install numpy\n",
        "! pip install pandas\n",
        "! pip install konlpy\n",
        "! pip install tqdm\n",
        "! pip install re\n",
        "! pip install gensim\n",
        "! pip install pyLDAvis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtiFRf5I-nYX",
        "outputId": "5a71ff13-d9b6-47fa-a385-bc89b24d1d6c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/site-packages (1.26.1)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: pandas in /usr/local/lib/python3.10/site-packages (2.1.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas) (2023.3.post1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/site-packages (from pandas) (1.26.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/site-packages (from pandas) (2023.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: konlpy in /usr/local/lib/python3.10/site-packages (0.6.0)\n",
            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.10/site-packages (from konlpy) (1.26.1)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.10/site-packages (from konlpy) (4.9.3)\n",
            "Requirement already satisfied: JPype1>=0.7.0 in /usr/local/lib/python3.10/site-packages (from konlpy) (1.4.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/site-packages (from JPype1>=0.7.0->konlpy) (23.2)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: tqdm in /usr/local/lib/python3.10/site-packages (4.65.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m\u001b[31mERROR: Could not find a version that satisfies the requirement re (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for re\u001b[0m\u001b[31m\n",
            "\u001b[0mRequirement already satisfied: gensim in /usr/local/lib/python3.10/site-packages (4.3.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/site-packages (from gensim) (1.26.1)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/site-packages (from gensim) (1.11.3)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/site-packages (from gensim) (6.4.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: pyLDAvis in /usr/local/lib/python3.10/site-packages (3.4.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (65.6.3)\n",
            "Requirement already satisfied: numpy>=1.24.2 in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (1.26.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (1.3.2)\n",
            "Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (2.1.2)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (1.3.2)\n",
            "Requirement already satisfied: funcy in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (2.0)\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (4.3.2)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (2.8.7)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (3.1.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/site-packages (from pyLDAvis) (1.11.3)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/site-packages (from pandas>=2.0.0->pyLDAvis) (2023.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/site-packages (from pandas>=2.0.0->pyLDAvis) (2023.3.post1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/site-packages (from pandas>=2.0.0->pyLDAvis) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/site-packages (from scikit-learn>=1.0.0->pyLDAvis) (3.2.0)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/site-packages (from gensim->pyLDAvis) (6.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/site-packages (from jinja2->pyLDAvis) (2.1.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->pyLDAvis) (1.16.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')\n",
        "from tqdm import tqdm\n",
        "import re\n",
        "from gensim import corpora\n",
        "import gensim\n",
        "import pyLDAvis.gensim_models\n",
        "from collections import Counter"
      ],
      "metadata": {
        "id": "wyW_HByS-oK0"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "앞으로 사용할 패키지를 설치하는 과정입니다."
      ],
      "metadata": {
        "id": "7es7YM7is_0z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 데이터 불러오기"
      ],
      "metadata": {
        "id": "IUAutz00svVf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw = pd.read_csv('reviews.csv') #본인 경로, 본인이 설정해 둔 파일명으로 수정\n",
        "dataset_raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "Epcm0i4n-rmq",
        "outputId": "da142111-b104-43a6-9f7e-b690a85aac82"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               reviewId       userName  \\\n",
              "0  eecc1d6f-2e1b-4d5c-bf06-e2ce6718c410   Krista Clark   \n",
              "1  48f76b7b-234f-474d-95db-e7ed321576bc  A Google user   \n",
              "2  1177566d-6443-42ab-8320-7e8b3342cec8  A Google user   \n",
              "3  d334cc2b-7e78-4b8b-9d11-ff198873a726  A Google user   \n",
              "4  82d2f0c3-dbff-4722-89fa-d8af997ba4ab      Hyco Syco   \n",
              "\n",
              "                                           userImage  \\\n",
              "0  https://play-lh.googleusercontent.com/a/ACg8oc...   \n",
              "1  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
              "2  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
              "3  https://play-lh.googleusercontent.com/EGemoI2N...   \n",
              "4  https://play-lh.googleusercontent.com/a/ACg8oc...   \n",
              "\n",
              "                                             content  score  thumbsUpCount  \\\n",
              "0  I used to love this app, but recently they did...      1            162   \n",
              "1  Love the interface, love the concept. But it l...      1             23   \n",
              "2  Product images show the month view at the top ...      1             13   \n",
              "3  What an awful UI. When creating a new reminder...      1              7   \n",
              "4  This app used to be great when paired with Ale...      1             10   \n",
              "\n",
              "  reviewCreatedVersion                   at  \\\n",
              "0           5.17.0.119  2023-07-02 17:35:08   \n",
              "1            4.15.4.11  2019-06-26 02:19:46   \n",
              "2                  NaN  2019-07-06 21:14:32   \n",
              "3             4.10.8.0  2018-12-13 19:16:15   \n",
              "4             4.15.9.6  2020-06-17 17:32:55   \n",
              "\n",
              "                                        replyContent            repliedAt  \\\n",
              "0  You should only see the upgrade ad when you fi...  2023-07-03 09:28:29   \n",
              "1  We are sorry to hear that you experience async...  2019-06-30 04:44:45   \n",
              "2  We appreciate the feedback. The images for iOS...  2019-07-09 11:45:56   \n",
              "3  To set a reminder, you only need to tap an exi...  2018-12-18 14:04:37   \n",
              "4  Sorry about that, our team is currently workin...  2020-06-18 06:47:52   \n",
              "\n",
              "   appVersion      sortOrder      appId  \n",
              "0  5.17.0.119  most_relevant  com.anydo  \n",
              "1   4.15.4.11  most_relevant  com.anydo  \n",
              "2         NaN  most_relevant  com.anydo  \n",
              "3    4.10.8.0  most_relevant  com.anydo  \n",
              "4    4.15.9.6  most_relevant  com.anydo  "
            ],
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              "      <td>eecc1d6f-2e1b-4d5c-bf06-e2ce6718c410</td>\n",
              "      <td>Krista Clark</td>\n",
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              "      <td>I used to love this app, but recently they did...</td>\n",
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              "      <td>Love the interface, love the concept. But it l...</td>\n",
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              "      <td>4.15.4.11</td>\n",
              "      <td>2019-06-26 02:19:46</td>\n",
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              "      <td>2019-06-30 04:44:45</td>\n",
              "      <td>4.15.4.11</td>\n",
              "      <td>most_relevant</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1177566d-6443-42ab-8320-7e8b3342cec8</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>https://play-lh.googleusercontent.com/EGemoI2N...</td>\n",
              "      <td>Product images show the month view at the top ...</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019-07-06 21:14:32</td>\n",
              "      <td>We appreciate the feedback. The images for iOS...</td>\n",
              "      <td>2019-07-09 11:45:56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>d334cc2b-7e78-4b8b-9d11-ff198873a726</td>\n",
              "      <td>A Google user</td>\n",
              "      <td>https://play-lh.googleusercontent.com/EGemoI2N...</td>\n",
              "      <td>What an awful UI. When creating a new reminder...</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>4.10.8.0</td>\n",
              "      <td>2018-12-13 19:16:15</td>\n",
              "      <td>To set a reminder, you only need to tap an exi...</td>\n",
              "      <td>2018-12-18 14:04:37</td>\n",
              "      <td>4.10.8.0</td>\n",
              "      <td>most_relevant</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>82d2f0c3-dbff-4722-89fa-d8af997ba4ab</td>\n",
              "      <td>Hyco Syco</td>\n",
              "      <td>https://play-lh.googleusercontent.com/a/ACg8oc...</td>\n",
              "      <td>This app used to be great when paired with Ale...</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>4.15.9.6</td>\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3264ac06-55ee-4332-8611-fc8477fe4d78 button');\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* pd.read_excel(’엑셀 파일 디렉토리’) : 엑셀 파일 가져오기 함수\n",
        "\n",
        "* head() : 상위 5행을 출력"
      ],
      "metadata": {
        "id": "aq9Thz4ctiui"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "분석을 할 때 사용할 열만 남겨주는 작업을 해야 합니다.\n",
        "리뷰 내용인 content, 평점인 score, 어떤 앱인지 appId를 제외한 열은 제거해줍니다."
      ],
      "metadata": {
        "id": "amibKktotP0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_drop = ['reviewId', 'userName', 'userImage', 'thumbsUpCount', 'reviewCreatedVersion', 'at','replyContent', 'repliedAt', 'appVersion', 'sortOrder']\n",
        "\n",
        "dataset_raw = dataset_raw.drop(columns_to_drop, axis=1)\n",
        "\n",
        "dataset_raw.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "xqiFLH2YDzZZ",
        "outputId": "7e4be92e-da83-4813-8d95-112e57ccda46"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
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              "                                             content  score      appId\n",
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              "        document.querySelector('#df-af3f2bac-2b31-4e4e-a3af-a82f272e1746 button.colab-df-convert');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4ca5f66d-f948-4f73-9862-6e2997f95eb2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결측치 존재 여부, 데이터 타입, 데이터 개수를 확인합니다."
      ],
      "metadata": {
        "id": "4WKZXqG2tlXT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0O1hGYj1U5jX",
        "outputId": "c82205cd-25c8-465f-a0bc-3c17b27a37e5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17608 entries, 0 to 17607\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   content  17607 non-null  object\n",
            " 1   score    17608 non-null  int64 \n",
            " 2   appId    17608 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 412.8+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_raw.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y4oGnuLZU_h4",
        "outputId": "cf0a33ee-f1c5-4e6d-ce5a-dd58e7c3e9fe"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "content    1\n",
              "score      0\n",
              "appId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "결측치를 제거해줍니다."
      ],
      "metadata": {
        "id": "3WoYewrdts8c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# axis = 0: 결측치 포함한 모든 행 제거\n",
        "dataset = dataset_raw.dropna(axis = 0)\n",
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHgix0oTt27k",
        "outputId": "3f82ba99-2ad7-44aa-ad5c-790e4dee26c7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "content    0\n",
              "score      0\n",
              "appId      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "fvO1M-u2uIGr",
        "outputId": "cce0020d-329f-4dcb-94e3-4ea5ae36b68f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 content  score  \\\n",
              "0      I used to love this app, but recently they did...      1   \n",
              "1      Love the interface, love the concept. But it l...      1   \n",
              "2      Product images show the month view at the top ...      1   \n",
              "3      What an awful UI. When creating a new reminder...      1   \n",
              "4      This app used to be great when paired with Ale...      1   \n",
              "...                                                  ...    ...   \n",
              "17603                                                Fun      5   \n",
              "17604                        So helpful with planning!!!      5   \n",
              "17605                                         Great app.      5   \n",
              "17606  If you are bipolar and seeking something to he...      5   \n",
              "17607  It's pretty great, helps me keep track of ever...      5   \n",
              "\n",
              "                   appId  \n",
              "0              com.anydo  \n",
              "1              com.anydo  \n",
              "2              com.anydo  \n",
              "3              com.anydo  \n",
              "4              com.anydo  \n",
              "...                  ...  \n",
              "17603  com.appxy.planner  \n",
              "17604  com.appxy.planner  \n",
              "17605  com.appxy.planner  \n",
              "17606  com.appxy.planner  \n",
              "17607  com.appxy.planner  \n",
              "\n",
              "[17607 rows x 3 columns]"
            ],
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              "<style scoped>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Product images show the month view at the top ...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What an awful UI. When creating a new reminder...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This app used to be great when paired with Ale...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17603</th>\n",
              "      <td>Fun</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17604</th>\n",
              "      <td>So helpful with planning!!!</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17605</th>\n",
              "      <td>Great app.</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17606</th>\n",
              "      <td>If you are bipolar and seeking something to he...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17607</th>\n",
              "      <td>It's pretty great, helps me keep track of ever...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17607 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a0d0ef2-f2a7-46b2-950b-fcc1399151cc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3a0d0ef2-f2a7-46b2-950b-fcc1399151cc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3a0d0ef2-f2a7-46b2-950b-fcc1399151cc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e1297273-0e0d-428a-a759-e25f23077a8b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1297273-0e0d-428a-a759-e25f23077a8b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1297273-0e0d-428a-a759-e25f23077a8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* dropna() : 결측값을 제거하는 메소드\n",
        "\n",
        "* axis=0의 의미 ⇒ axis 값이 0일 경우 행 방향으로 작업을 수행하라는 의미이다. 따라서 행에 대해 결측값이 있는지 확인하고 결측값이 있는 행을 제거한다."
      ],
      "metadata": {
        "id": "i-bLHQMBt7rq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**요약**\n",
        "\n",
        "위에서 결측치가 있는 것을 확인했기 때문에 결측치를 포함한 모든 행을 제거하고 올바르게 제거가 되었는지 확인했습니다."
      ],
      "metadata": {
        "id": "x4iRABZYt-mX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 불용어 처리"
      ],
      "metadata": {
        "id": "bvPIFnnMVVES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "불용어를 처리해주는 패키지를 설치하고 불러와줍니다."
      ],
      "metadata": {
        "id": "aBioBYBtZzcm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install spacy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QOHJpe8W5Um",
        "outputId": "bfe8a791-a77b-419e-a9c3-7f3614f79a28"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: spacy in /usr/local/lib/python3.10/site-packages (3.7.2)\n",
            "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/site-packages (from spacy) (0.3.3)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.1.8 in /usr/local/lib/python3.10/site-packages (from spacy) (8.2.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/site-packages (from spacy) (65.6.3)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/site-packages (from spacy) (2.4.2)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/site-packages (from spacy) (0.9.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/site-packages (from spacy) (1.0.10)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/site-packages (from spacy) (3.0.9)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/site-packages (from spacy) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/site-packages (from spacy) (2.4.8)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/site-packages (from spacy) (3.3.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/site-packages (from spacy) (23.2)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/site-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/site-packages (from spacy) (2.0.10)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/site-packages (from spacy) (3.1.2)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/site-packages (from spacy) (1.26.1)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/site-packages (from spacy) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/site-packages (from spacy) (4.65.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/site-packages (from spacy) (2.28.2)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/site-packages (from spacy) (2.0.8)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/site-packages (from spacy) (3.0.12)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (4.8.0)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.10.1 in /usr/local/lib/python3.10/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy) (2.10.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy) (3.4)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/site-packages (from requests<3.0.0,>=2.13.0->spacy) (1.26.15)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/site-packages (from thinc<8.3.0,>=8.1.8->spacy) (0.1.3)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/site-packages (from thinc<8.3.0,>=8.1.8->spacy) (0.7.11)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/site-packages (from typer<0.10.0,>=0.3.0->spacy) (8.1.7)\n",
            "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/site-packages (from weasel<0.4.0,>=0.1.0->spacy) (0.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/site-packages (from jinja2->spacy) (2.1.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "# spacy 모델 로드\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "# 불용어 설정 - 기본 영어 불용어 사용\n",
        "customize_stop_words = nlp.Defaults.stop_words"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dz9q3QtUW8f9",
        "outputId": "f7b2ffb4-3ca9-472f-d8de-e2d470f15767"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for w in customize_stop_words:\n",
        "    nlp.vocab[w].is_stop = True\n",
        "\n",
        "# 텍스트 데이터에 불용어 처리 적용\n",
        "def remove_stopwords(text):\n",
        "    doc = nlp(text.lower())\n",
        "    without_stopwords = \" \".join([token.text for token in doc if not token.is_stop and len(token.text) > 1])\n",
        "    return without_stopwords"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HYYsGnClu2py",
        "outputId": "cf9d01bc-7373-48d9-f04b-67539756f6df"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "어간 추출은 spacy 라이브러리의 lemma_ 속성을 사용하여 수행합니다. 이 속성은 단어의 기본 형태인 \"표제어\"를 반환합니다."
      ],
      "metadata": {
        "id": "NdUtf8uHy5ni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 'content' 열의 각 데이터에 대해 불용어 처리\n",
        "processed_reviews = []\n",
        "for review in dataset['content']:\n",
        "    if isinstance(review, str):  # 문자열인 경우에만 처리\n",
        "        processed_reviews.append(remove_stopwords(review))\n",
        "    else:\n",
        "        processed_reviews.append('')  # NaN 또는 빈 값인 경우 빈 문자열로 처리\n",
        "\n",
        "# 불용어가 제거된 데이터를 새로운 열로 추가\n",
        "dataset['review_prep'] = processed_reviews\n",
        "\n",
        "# NaN 값을 빈 문자열로 처리\n",
        "dataset['review_prep'] = dataset['review_prep'].apply(lambda x: '' if pd.isna(x) else x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qd8RvwQRwfLS",
        "outputId": "62abba23-4c24-443d-db9f-9eb1e3e84540"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결과 확인\n",
        "print(dataset['review_prep'].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgYLGJ_KvLJN",
        "outputId": "6b64c58b-5ce7-4175-e790-778bdbb0f2bc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    love app recently big push premium time starte...\n",
            "1    love interface love concept lacks key features...\n",
            "2    product images month view screen dots represen...\n",
            "3    awful ui creating new reminder entire screen d...\n",
            "4    app great paired alexa awhile sync break disab...\n",
            "Name: review_prep, dtype: object\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "불용어 리스트에 있는 단어들이 제거된 것을 볼 수 있습니다."
      ],
      "metadata": {
        "id": "Hb2yj3JjZ5Mx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2864
        },
        "id": "XDYy6oqixyA9",
        "outputId": "ad140b60-b9e8-4bfb-fce7-4feccafa7a64"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 content  score  \\\n",
              "0      I used to love this app, but recently they did...      1   \n",
              "1      Love the interface, love the concept. But it l...      1   \n",
              "2      Product images show the month view at the top ...      1   \n",
              "3      What an awful UI. When creating a new reminder...      1   \n",
              "4      This app used to be great when paired with Ale...      1   \n",
              "...                                                  ...    ...   \n",
              "17603                                                Fun      5   \n",
              "17604                        So helpful with planning!!!      5   \n",
              "17605                                         Great app.      5   \n",
              "17606  If you are bipolar and seeking something to he...      5   \n",
              "17607  It's pretty great, helps me keep track of ever...      5   \n",
              "\n",
              "                   appId                                        review_prep  \n",
              "0              com.anydo  love app recently big push premium time starte...  \n",
              "1              com.anydo  love interface love concept lacks key features...  \n",
              "2              com.anydo  product images month view screen dots represen...  \n",
              "3              com.anydo  awful ui creating new reminder entire screen d...  \n",
              "4              com.anydo  app great paired alexa awhile sync break disab...  \n",
              "...                  ...                                                ...  \n",
              "17603  com.appxy.planner                                                fun  \n",
              "17604  com.appxy.planner                                   helpful planning  \n",
              "17605  com.appxy.planner                                          great app  \n",
              "17606  com.appxy.planner  bipolar seeking help track events tasks projec...  \n",
              "17607  com.appxy.planner      pretty great helps track great cause adhd ool  \n",
              "\n",
              "[17607 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-65f36765-8ac7-4dfc-9d5a-85cd7b1891d9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>appId</th>\n",
              "      <th>review_prep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I used to love this app, but recently they did...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>love app recently big push premium time starte...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love the interface, love the concept. But it l...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>love interface love concept lacks key features...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Product images show the month view at the top ...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>product images month view screen dots represen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What an awful UI. When creating a new reminder...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>awful ui creating new reminder entire screen d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This app used to be great when paired with Ale...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>app great paired alexa awhile sync break disab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17603</th>\n",
              "      <td>Fun</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>fun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17604</th>\n",
              "      <td>So helpful with planning!!!</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>helpful planning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17605</th>\n",
              "      <td>Great app.</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>great app</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17606</th>\n",
              "      <td>If you are bipolar and seeking something to he...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>bipolar seeking help track events tasks projec...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17607</th>\n",
              "      <td>It's pretty great, helps me keep track of ever...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>pretty great helps track great cause adhd ool</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17607 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-65f36765-8ac7-4dfc-9d5a-85cd7b1891d9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-65f36765-8ac7-4dfc-9d5a-85cd7b1891d9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-65f36765-8ac7-4dfc-9d5a-85cd7b1891d9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2d1ec95d-de46-4c48-a892-cee9b6ddf6fe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2d1ec95d-de46-4c48-a892-cee9b6ddf6fe')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2d1ec95d-de46-4c48-a892-cee9b6ddf6fe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7d45060750f0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "dataset['score'].plot(kind='hist', bins=20, title='score')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
              "      <div class=\"colab-quickchart-chart-with-code\" id=\"chart-f7113c3b-011d-4988-91dc-29e91a5a2754\">\n",
              "        <img style=\"width: 180px;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlQAAAG8CAYAAAAYZztbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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              "9957ryZPnqx69eo5a7Ozs/Xmm29q586dunDhgsLCwtSrVy9NnjxZLVu2dNn+4sWL9frrr2vFihX6\n",
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              "DcAtI1AB8Gtr165VYGCg0tLS1KhRI5d158+fdz7+/e9/r9zcXP3ud7/TxIkTy9XVr19fkmS32/WH\n",
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              "cEF2u13R0dEaN26cxo8fr0aNGt20jR/6dRhgt9vtbm0RAADgR4ZrqAAAAAwRqAAAAAwRqAAAAAwR\n",
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              "AAz9fySTGkxMGtYjAAAAAElFTkSuQmCC\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f7113c3b-011d-4988-91dc-29e91a5a2754\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f7113c3b-011d-4988-91dc-29e91a5a2754\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7d4506074fa0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "dataset['score'].plot(kind='line', figsize=(8, 4), title='score')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "S/3/85Av9qKPpOwAAAAASUVORK5CYII=\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-44eaa1b2-4195-4093-98e6-3f4fab2b4512\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-44eaa1b2-4195-4093-98e6-3f4fab2b4512\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "전체 데이터셋도 함께 확인해 보았습니다."
      ],
      "metadata": {
        "id": "YgO_GK_GyR8a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## 표제어 추출 (어형이 과거형이든 미래형이든 하나의 단어로 취급하기 위한 처리작업)"
      ],
      "metadata": {
        "id": "r8nHSdNS6fcA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "어간만 추출해줍니다. 또한 한 글자인 단어는 보통 오타나 의미가 없기 때문에 1글자 단어는 삭제해 줍니다."
      ],
      "metadata": {
        "id": "bzwix6lYZ8hh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 표제어 추출 및 길이가 1인 단어 삭제 함수 정의\n",
        "def process_text(text):\n",
        "    doc = nlp(text)\n",
        "    processed_text = ' '.join([token.lemma_ for token in doc if len(token.text) > 1])\n",
        "    return processed_text\n",
        "\n",
        "# 'review_prep' 열의 각 데이터에 대해 표제어 추출 수행\n",
        "dataset['review_prep'] = dataset['review_prep'].apply(process_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oZ8RNU16uR6",
        "outputId": "14a60db7-986f-4204-8314-6c092d6f2179"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "알파벳과 공백 이외의 문자를 제거합니다."
      ],
      "metadata": {
        "id": "JWKITJT2L6YB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "# 함수: 알파벳과 공백만 남기고 나머지 제거\n",
        "def keep_only_alphanumeric(text):\n",
        "    # 알파벳과 공백을 제외한 모든 문자 제거\n",
        "    cleaned_text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "    return cleaned_text\n",
        "\n",
        "# 각 리뷰에 대해 알파벳과 공백만 남기고 나머지 제거\n",
        "dataset['review_prep'] = dataset['review_prep'].apply(keep_only_alphanumeric)\n",
        "\n",
        "# 결과 확인\n",
        "print(dataset['review_prep'].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35cUXPuYLsfE",
        "outputId": "29e62a7f-c1b8-44be-9dbc-d4264b366ca5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    love app recently big push premium time start ...\n",
            "1    love interface love concept lack key feature a...\n",
            "2    product image month view screen dot represent ...\n",
            "3    awful ui create new reminder entire screen dis...\n",
            "4    app great pair alexa awhile sync break disable...\n",
            "Name: review_prep, dtype: object\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "qyzkoqcZ3lst",
        "outputId": "418f4aea-54a1-4d22-f57c-839d6ebed96a"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 content  score  \\\n",
              "0      I used to love this app, but recently they did...      1   \n",
              "1      Love the interface, love the concept. But it l...      1   \n",
              "2      Product images show the month view at the top ...      1   \n",
              "3      What an awful UI. When creating a new reminder...      1   \n",
              "4      This app used to be great when paired with Ale...      1   \n",
              "...                                                  ...    ...   \n",
              "17603                                                Fun      5   \n",
              "17604                        So helpful with planning!!!      5   \n",
              "17605                                         Great app.      5   \n",
              "17606  If you are bipolar and seeking something to he...      5   \n",
              "17607  It's pretty great, helps me keep track of ever...      5   \n",
              "\n",
              "                   appId                                        review_prep  \n",
              "0              com.anydo  love app recently big push premium time start ...  \n",
              "1              com.anydo  love interface love concept lack key feature a...  \n",
              "2              com.anydo  product image month view screen dot represent ...  \n",
              "3              com.anydo  awful ui create new reminder entire screen dis...  \n",
              "4              com.anydo  app great pair alexa awhile sync break disable...  \n",
              "...                  ...                                                ...  \n",
              "17603  com.appxy.planner                                                fun  \n",
              "17604  com.appxy.planner                                   helpful planning  \n",
              "17605  com.appxy.planner                                          great app  \n",
              "17606  com.appxy.planner  bipolar seek help track event task project day...  \n",
              "17607  com.appxy.planner      pretty great helps track great cause adhd ool  \n",
              "\n",
              "[17607 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9b8b05ce-12ba-4e5b-bacb-08dec137cc21\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>content</th>\n",
              "      <th>score</th>\n",
              "      <th>appId</th>\n",
              "      <th>review_prep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I used to love this app, but recently they did...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>love app recently big push premium time start ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Love the interface, love the concept. But it l...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>love interface love concept lack key feature a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Product images show the month view at the top ...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>product image month view screen dot represent ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>What an awful UI. When creating a new reminder...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>awful ui create new reminder entire screen dis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>This app used to be great when paired with Ale...</td>\n",
              "      <td>1</td>\n",
              "      <td>com.anydo</td>\n",
              "      <td>app great pair alexa awhile sync break disable...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17603</th>\n",
              "      <td>Fun</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>fun</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17604</th>\n",
              "      <td>So helpful with planning!!!</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>helpful planning</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17605</th>\n",
              "      <td>Great app.</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>great app</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17606</th>\n",
              "      <td>If you are bipolar and seeking something to he...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>bipolar seek help track event task project day...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17607</th>\n",
              "      <td>It's pretty great, helps me keep track of ever...</td>\n",
              "      <td>5</td>\n",
              "      <td>com.appxy.planner</td>\n",
              "      <td>pretty great helps track great cause adhd ool</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17607 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b8b05ce-12ba-4e5b-bacb-08dec137cc21')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9b8b05ce-12ba-4e5b-bacb-08dec137cc21 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9b8b05ce-12ba-4e5b-bacb-08dec137cc21');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c7c20c86-4921-4585-bcf1-7dd8b689a2cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c7c20c86-4921-4585-bcf1-7dd8b689a2cc')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7c20c86-4921-4585-bcf1-7dd8b689a2cc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 평점 기준 데이터 분리"
      ],
      "metadata": {
        "id": "SQESVrI7rtzD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 토큰화"
      ],
      "metadata": {
        "id": "7bsHI3YBrwg8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 평점 기준 데이터 분리"
      ],
      "metadata": {
        "id": "eL6eqXFPU4NI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "구글 플레이 스토어는 평점이 5점 만점입니다. 따라서 본 프로젝트에서는 4~5점을 긍정적인 평가를 받은 리뷰로, 1~2점을 부정적인 평가를 받은 리뷰로 분류하였습니다. 이는 서비스 이용 경험과 관련한 리뷰 내용이 긍정적인지, 부정적인지를 구분하기 위한 목적입니다."
      ],
      "metadata": {
        "id": "PhYg_KOwU8Ek"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "명사를 추출하여 review_tokenized_pos에 저장합니다."
      ],
      "metadata": {
        "id": "vbGNwLeP0Wxw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 긍정적 리뷰: 평점 4, 5점\n",
        "review_pos = dataset[(dataset['score'] == 4) | (dataset['score'] == 5)]['review_prep']\n",
        "\n",
        "# 명사만 추출하는 함수\n",
        "def extract_nouns(text):\n",
        "    doc = nlp(text)\n",
        "    nouns = [token.text for token in doc if token.pos_ in ['NOUN', 'PROPN']]\n",
        "    return nouns\n",
        "\n",
        "# 각 review_pos에 대해 명사 추출하여 review_tokenized_pos에 저장\n",
        "review_tokenized_pos = [extract_nouns(review) for review in review_pos]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACd-Xwhf0UnM",
        "outputId": "3680206e-650e-4864-bd8d-f94cad8f454a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 부정적 리뷰: 평점 1, 2점\n",
        "review_neg = dataset[(dataset['score'] == 1) | (dataset['score'] == 2)]['review_prep']\n",
        "\n",
        "# 명사만 추출하는 함수\n",
        "def extract_nouns(text):\n",
        "    doc = nlp(text)\n",
        "    nouns = [token.text for token in doc if token.pos_ in ['NOUN', 'PROPN']]\n",
        "    return nouns\n",
        "\n",
        "# 각 review_pos에 대해 명사 추출하여 review_tokenized_pos에 저장\n",
        "review_tokenized_neg = [extract_nouns(review) for review in review_neg]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_F-FDl92__Z",
        "outputId": "b286984f-5a2f-4f13-f9f4-357ed824e30c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**요약**\n",
        "\n",
        "긍정적인 리뷰와 부정적인 리뷰에서 명사만 추출하여 각각 review_tokenized_pos와 review_tokenized_neg에 저장했습니다."
      ],
      "metadata": {
        "id": "c27EyL3hVinu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 전처리 결과 확인"
      ],
      "metadata": {
        "id": "zYWSL2BbsAIV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_num_pos = len(review_tokenized_pos)\n",
        "review_num_neg = len(review_tokenized_neg)\n",
        "review_num_tot = review_num_pos + review_num_neg\n",
        "\n",
        "print(f\"분석한 리뷰 총 개수: {review_num_tot}\")\n",
        "print(f\"긍정적 리뷰: {review_num_pos}개({(review_num_pos/review_num_tot)*100:.2f}%)\")\n",
        "print(f\"부정적 리뷰: {review_num_neg}개({(review_num_neg/review_num_tot)*100:.2f}%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vu7MneGS8dPG",
        "outputId": "78b5b71f-301a-459d-ae59-2c854d4aba19"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "분석한 리뷰 총 개수: 11906\n",
            "긍정적 리뷰: 6000개(50.39%)\n",
            "부정적 리뷰: 5906개(49.61%)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "분석한 리뷰의 개수를 len함수를 통해 알아보겠습니다.\n",
        "\n",
        "review_num_pos와 review_num_neg를 더하면 총 리뷰가 되기 때문에 review_num_tot를 생성하고 print 함수를 통해 총 리뷰 개수와 긍정적 리뷰, 부정적 리뷰의 개수를 알아 보겠습니다."
      ],
      "metadata": {
        "id": "5kzdhbKzVlGl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "그 결과, 최초에 수집한 17,603개 리뷰 데이터 중 전처리 후 남은 데이터는 11,906개입니다.\n",
        "\n",
        "**요약**\n",
        "\n",
        "len함수와 print 함수를 통해 분석한 리뷰의 총 개수는 11,906개이고 긍정적 리뷰의 개수와 비율, 부정적 리뷰의 개수와 비율을 알 수 있었습니다."
      ],
      "metadata": {
        "id": "0W1qDDzpVs80"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LDA 토픽 모델링"
      ],
      "metadata": {
        "id": "xhSvmIv8sDge"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1) 하이퍼파라미터 튜닝"
      ],
      "metadata": {
        "id": "cVuCQFiesGfS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "토픽의 개수와 학습 횟수는 하이퍼파라미터입니다. 본 프로젝트에서는 긍정적 리뷰와 부정적 리뷰 각각에서 토픽 10개씩을 추출합니다. 학습 횟수는 PASSES 옵션으로 변경하며 전체 문서 데이터를 몇 번 학습할지 결정합니다.\n",
        "\n",
        "- 하이퍼파라미터 : 모델을 학습시키기 위해 사전에 사용자가 설정해야 하는 매개변수들\n",
        "\n",
        "- PASSES : 모델을 토픽 모델에 반복하여 입력하고 학습하는 단계의 수"
      ],
      "metadata": {
        "id": "wKJ4Ely98zQf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NUM_TOPICS = 10 # 토픽 개수는 하이퍼파라미터\n",
        "# passes: 딥러닝에서 Epoch와 같은 개념으로, 전체 corpus로 모델 학습 횟수 결정\n",
        "PASSES = 15"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uy-tdxxw8w8z",
        "outputId": "698e8a88-8421-4d35-864c-7f63452c3f61"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2) 모델 학습 함수"
      ],
      "metadata": {
        "id": "AizbzNkIsIKG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "lda_modeling은 우리가 만들 토픽 모델링을 수행하는 함수입니다."
      ],
      "metadata": {
        "id": "Yo-DeVM_VQAm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lda_modeling(review_prep):\n",
        "    # 단어 인코딩 및 빈도수 계산\n",
        "    dictionary = corpora.Dictionary(review_prep)\n",
        "    corpus = [dictionary.doc2bow(review) for review in review_prep]\n",
        "    # LDA 모델 학습\n",
        "    model = gensim.models.ldamodel.LdaModel(corpus,\n",
        "                                            num_topics = NUM_TOPICS,\n",
        "                                            id2word = dictionary,\n",
        "                                            passes = PASSES)\n",
        "    return model, corpus, dictionary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzD-ET8X85HO",
        "outputId": "bc05c564-d425-4265-fd0a-0a5cb1d1a97f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**코드 분석**\n",
        "\n",
        "1. `def lda_modeling(review_prep):`: `lda_modeling`이라는 함수를 정의하며, `review_prep`라는 매개변수를 받습니다. 이 매개변수는 토픽 모델링을 수행할 대상인 전처리된 리뷰 데이터입니다.\n",
        "2. `dictionary = corpora.Dictionary(review_prep)`: `corpora.Dictionary`를 사용하여 단어 사전(dictionary)을 생성합니다. 이 사전은 전처리된 리뷰 데이터에 있는 단어들을 고유한 정수로 매핑합니다.\n",
        "    \n",
        "    * corpora.Dictionary : Gensim 라이브러리에서 제공하는 도구로, 텍스트 문서를 표현하는 딕셔너리를 생성하는 역할\n",
        "    \n",
        "    - corpora 사용 예시\n",
        "        \n",
        "        ```python\n",
        "        from gensim import corpora\n",
        "        \n",
        "        # 간단한 텍스트 문서들\n",
        "        documents = [\n",
        "            ['apple', 'banana', 'cherry'],\n",
        "            ['banana', 'orange', 'kiwi'],\n",
        "            ['apple', 'cherry', 'orange', 'banana']\n",
        "        ]\n",
        "        \n",
        "        # Dictionary 생성\n",
        "        dictionary = corpora.Dictionary(documents)\n",
        "        \n",
        "        # 각 단어의 고유 ID 확인\n",
        "        print(dictionary.token2id)  # 단어에 대한 ID 매핑\n",
        "        \n",
        "        # 특정 단어의 ID 얻기\n",
        "        print(dictionary.token2id.get('banana'))  # banana에 대한 ID\n",
        "        ```\n",
        "        \n",
        "        이렇게 생성된 딕셔너리를 사용하여 텍스트 데이터를 수치화하여 다양한 자연어 처리 작업에 활용할 수 있습니다.\n",
        "        \n",
        "3. `corpus = [dictionary.doc2bow(review) for review in review_prep]`: 단어 빈도수 계산을 위해 `doc2bow`를 사용하여 각 리뷰를 단어 빈도수 형태(corpus)로 변환합니다. 이 형태는 단어의 인덱스와 해당 단어의 빈도수를 포함합니다.\n",
        "    \n",
        "    * doc2bow : Gensim 라이브러리에서 제공하는 메서드로, 문서를 Bag-of-Words 형태로 변환하는 역할을 합니다. Bag-of-Words는 텍스트 데이터를 단어의 등장 빈도로 표현한 것으로, 단어의 등장 순서는 고려하지 않습니다.\n",
        "    \n",
        "    - doc2bow 사용 예시\n",
        "        \n",
        "        ```python\n",
        "        from gensim import corpora\n",
        "        \n",
        "        # Dictionary 생성 (단어에 ID 부여)\n",
        "        dictionary = corpora.Dictionary([\n",
        "            ['apple', 'banana', 'cherry'],\n",
        "            ['banana', 'orange', 'kiwi'],\n",
        "            ['apple', 'cherry', 'orange', 'banana']\n",
        "        ])\n",
        "        \n",
        "        # 문서 예시\n",
        "        document = ['apple', 'banana', 'banana', 'cherry']\n",
        "        \n",
        "        # 문서를 Bag-of-Words로 변환\n",
        "        bow_document = dictionary.doc2bow(document)\n",
        "        print(bow_document)\n",
        "        ```\n",
        "        \n",
        "        출력\n",
        "        \n",
        "        ```python\n",
        "        [(0, 1), (1, 2), (2, 1)]\n",
        "        ```\n",
        "        \n",
        "4. LDA 모델 학습:\n",
        "    - `model = gensim.models.ldamodel.LdaModel(...)`: `gensim` 라이브러리를 사용하여 LDA 모델을 생성합니다.\n",
        "    - `corpus`: 앞서 변환한 단어 빈도수 형태(corpus)를 입력 데이터로 사용합니다.\n",
        "    - `num_topics = NUM_TOPICS`: 토픽의 개수를 설정합니다. `NUM_TOPICS`는 미리 정의된 상수(10 하이퍼파라미터 튜닝에서 정의함)로, 토픽의 개수를 나타냅니다.\n",
        "    - `id2word = dictionary`: 앞서 생성한 단어 사전을 사용하여 단어와 단어의 인덱스를 연결합니다.\n",
        "    - `passes = PASSES`: LDA 모델 학습을 위해 데이터를 반복적으로 사용하는 횟수를 나타냅니다. `PASSES`는 미리 정의된 상수(15 하이퍼파라미터 튜닝에서 정의함)로, 학습 반복 횟수를 나타냅니다.\n",
        "5. `return model, corpus, dictionary`: 학습된 LDA 모델(`model`), 단어 빈도수 형태(`corpus`), 그리고 단어 사전(`dictionary`)을 반환합니다."
      ],
      "metadata": {
        "id": "rSkFiqBVVYqH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3) 토픽별 단어 구성 출력 함수"
      ],
      "metadata": {
        "id": "tY36Yf34sKLR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "\n",
        "def print_topic_prop(topics, RATING):\n",
        "    topic_values = [topic[1] for topic in topics]\n",
        "    topic_prop = pd.DataFrame({\"topic_num\": list(range(1, NUM_TOPICS + 1)), \"word_prop\": topic_values})\n",
        "\n",
        "    # Excel 파일로 저장, 본인 경로로 수정 필요\n",
        "    file_path = f'/content/drive/MyDrive/dongguk_english/topic_prop_lemmatizer_{RATING}.xlsx'\n",
        "    topic_prop.to_excel(file_path)\n",
        "\n",
        "    # 파일을 다운로드\n",
        "    files.download(file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeIRBKOo89as",
        "outputId": "950f55cc-0341-4809-8bde-dda6474b4cfe"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4) 시각화 함수"
      ],
      "metadata": {
        "id": "yGr1euBDsMlp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim_models\n",
        "\n",
        "def lda_visualize(model, corpus, dictionary, RATING, result_dir):\n",
        "    pyLDAvis.enable_notebook()\n",
        "    result_visualized = pyLDAvis.gensim_models.prepare(model, corpus, dictionary)\n",
        "    pyLDAvis.display(result_visualized)\n",
        "\n",
        "    # 시각화 결과 저장\n",
        "    RESULT_FILE = os.path.join(result_dir, f'lda_result_lemmatizer_{RATING}.html')\n",
        "    pyLDAvis.save_html(result_visualized, RESULT_FILE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kx5ygzCV9EZJ",
        "outputId": "6f9d5554-d5df-43cf-c5de-4c878cf8c9ae"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5) 긍정적 리뷰 토픽 모델링"
      ],
      "metadata": {
        "id": "PW0Mqs98sOOG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model, corpus, dictionary = lda_modeling(review_tokenized_pos)\n",
        "NUM_WORDS = 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iKirueKg9GvH",
        "outputId": "4da2f454-0a36-4184-e412-7953143eecb7"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RATING = 'pos'\n",
        "topics = model.print_topics(num_words = NUM_WORDS)\n",
        "print_topic_prop(topics, RATING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "rPkb0Z-t9PSM",
        "outputId": "b7769e14-0327-480a-f41b-af2eb4ee6a79"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_c6eed2cd-756f-4b4a-8707-639b5cd24e64\", \"topic_prop_lemmatizer_pos.xlsx\", 5654)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_visualize(model, corpus, dictionary, RATING, '/content/drive/MyDrive/dongguk_english/')\n",
        "# 마지막 요소 본인 경로로 수정 필요"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9s0NZZT9RgP",
        "outputId": "a1aa2a98-780a-4081-f101-2645ee83bcfe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6) 부정적 리뷰 토픽 모델링"
      ],
      "metadata": {
        "id": "miDAQH-asP6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model, corpus, dictionary = lda_modeling(review_tokenized_neg)\n",
        "NUM_WORDS = 10"
      ],
      "metadata": {
        "id": "oviiJCe69LHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "RATING = 'neg'\n",
        "topics = model.print_topics(num_words = NUM_WORDS)\n",
        "print_topic_prop(topics, RATING)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "Qza4_PwA9NyK",
        "outputId": "8668a71c-08fe-4489-c644-509e6f3aecc1"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_0ee17d0e-46c0-47e5-87a6-f75def6ff024\", \"topic_prop_lemmatizer_neg.xlsx\", 5654)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_visualize(model, corpus, dictionary, RATING,'/content/drive/MyDrive/dongguk_english/')\n",
        "# 마지막 요소 본인 경로로 수정 필요"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fPl51d0J9VGG",
        "outputId": "93c3098d-7d1b-4b70-f339-73ea4a6c1561"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    }
  ]
}