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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anil951/YouTube-Video-Recommendation-for-Domain-Specific-Content/blob/main/yt_rank_main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install virtualenv\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "!virtualenv /content/drive/MyDrive/vir_env\n",
        "\n",
        "!source /content/drive/MyDrive/vir_env/bin/activate; pip install pattern\n",
        "\n",
        "#after installing the libraries to google drive you can just run the above code and use those libraries without installing them again."
      ],
      "metadata": {
        "id": "WhmLJ62vxAMX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# run this everytime a new runtime is enabled, no need to install all again\n",
        "\n",
        "# the above cell need not to be run everytime(run only once when time of installation)\n",
        "\n",
        "# it has ,\n",
        "# contractions,\n",
        "# indic-transliteration\n",
        "# langdetect\n",
        "# googletrans\n",
        "# iso639-lang\n",
        "# textblob\n",
        "# pytube\n",
        "# polyglot\n",
        "# googletrans (pip install googletrans==4.0.0-rc1)\n",
        "# pycld2 (pip install pycld2)\n",
        "# iso-639 (pip install iso-639)\n",
        "# pycountry (pip install pycountry)  -- to get lang-id to lamguage\n",
        "# multilingual-sentiment-classifier (pip install multilingual-sentiment-classifier)\n",
        "# pattern (for sentiment analysis)\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"/content/drive/MyDrive/vir_env/lib/python3.10/site-packages\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgvSudE1vC4Y",
        "outputId": "aff27e04-1bab-4e0d-ff26-9e91cc0ca145"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting youtube video id from youtube video link(url)\n",
        "\n",
        "url1='https://youtu.be/unxAnJBy12Q?si=PAI-gEhmZjDa3u_Z'\n",
        "url2='https://youtu.be/bOk35XmHPKs?si=BWuV9I5CjQZ9zL03'\n",
        "url3='https://youtu.be/3j0SWDX4AtU?si=nJmRfDwF764fiFD_'\n",
        "url4='https://youtu.be/mB5HXBb_HY8?si=NA-51KIKtRr2XVBR'\n",
        "url5='https://youtu.be/iKGAgWdgoRk?si=JgbpVerjrK6KPMpp'\n",
        "\n",
        "urls=[url1,url2,url3,url4,url5]\n",
        "video_ids=[]\n",
        "\n",
        "#Get youtube id\n",
        "from pytube import extract\n",
        "for url in urls:\n",
        "    video_ids.append(extract.video_id(url))\n",
        "\n",
        "print(video_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upiAXQEcxEX8",
        "outputId": "24f15d6f-7a84-4724-b3a8-aebf1efd7ab5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['unxAnJBy12Q', 'bOk35XmHPKs', '3j0SWDX4AtU', 'mB5HXBb_HY8', 'iKGAgWdgoRk']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import googleapiclient.discovery\n",
        "import re\n",
        "\n",
        "# Set your API key (replace 'YOUR_API_KEY' with your actual key)\n",
        "api_key = 'AIzaSyC1XMRCkSRnNf-XnWvYgJZUTLaARwFeO68'\n",
        "\n",
        "# Initialize the YouTube API client\n",
        "youtube = googleapiclient.discovery.build('youtube', 'v3', developerKey=api_key)\n",
        "\n",
        "# List of video links or IDs\n",
        "# video_ids = [ 'aml9xZW8zqE', 'fL7kgMMiAxI','NqBRS2_cNlQ','3oNzkS1WYas','YTrQ6rQX5yY']  # Replace with the actual video IDs\n",
        "\n",
        "# Initialize lists to store video details\n",
        "video_details = []\n",
        "\n",
        "# Maximum allowed comment length (in words)\n",
        "max_comment_length = 100\n",
        "\n",
        "# Regular expression to match only characters (letters)\n",
        "char_pattern = re.compile(r'[A-Za-z]+')\n",
        "\n",
        "# Function to convert ISO 8601 duration to seconds\n",
        "def iso8601_to_seconds(duration):\n",
        "    match = re.match(r'PT(\\d+H)?(\\d+M)?(\\d+S)?', duration)\n",
        "    hours = int(match.group(1)[:-1]) if match.group(1) else 0\n",
        "    minutes = int(match.group(2)[:-1]) if match.group(2) else 0\n",
        "    seconds = int(match.group(3)[:-1]) if match.group(3) else 0\n",
        "    return hours * 3600 + minutes * 60 + seconds\n",
        "\n",
        "# Loop through the video IDs and fetch video details\n",
        "for video_id in video_ids:\n",
        "    try:\n",
        "        # Fetch video details (snippet, statistics, and content details)\n",
        "        video_response = youtube.videos().list(\n",
        "            part='snippet,statistics,contentDetails',\n",
        "            id=video_id\n",
        "        ).execute()\n",
        "\n",
        "        if 'items' in video_response and len(video_response['items']) > 0:\n",
        "            video = video_response['items'][0]\n",
        "\n",
        "            # Extract video details\n",
        "            title = video['snippet']['title']\n",
        "            tags = video['snippet']['tags'] if 'tags' in video['snippet'] else []\n",
        "            likes = int(video['statistics']['likeCount']) if 'likeCount' in video['statistics'] else 0\n",
        "            dislikes = int(video['statistics']['dislikeCount']) if 'dislikeCount' in video['statistics'] else 0\n",
        "            duration_iso = video['contentDetails']['duration']  # Video duration in ISO 8601 format (e.g., \"PT10M45S\")\n",
        "\n",
        "            # Convert ISO 8601 duration to seconds (total video duration)\n",
        "            total_duration_seconds = iso8601_to_seconds(duration_iso)\n",
        "\n",
        "            # Get the number of views\n",
        "            views = int(video['statistics']['viewCount']) if 'viewCount' in video['statistics'] else 0\n",
        "\n",
        "            # Fetch all comments using the YouTube API (paginate through comments)\n",
        "            comments = []\n",
        "            nextPageToken = None\n",
        "            while True:\n",
        "                comments_response = youtube.commentThreads().list(\n",
        "                    part='snippet',\n",
        "                    videoId=video_id,\n",
        "                    textFormat='plainText',\n",
        "                    maxResults=100,  # Adjust as needed (max is 100)\n",
        "                    pageToken=nextPageToken\n",
        "                ).execute()\n",
        "\n",
        "                for comment in comments_response['items']:\n",
        "                    text_display = comment['snippet']['topLevelComment']['snippet']['textDisplay']\n",
        "                    if (\n",
        "                        len(text_display.split()) <= max_comment_length and\n",
        "                        char_pattern.match(text_display)\n",
        "                    ):\n",
        "                        comments.append(text_display)\n",
        "\n",
        "                nextPageToken = comments_response.get('nextPageToken')\n",
        "                if not nextPageToken:\n",
        "                    break\n",
        "\n",
        "            # Get the total number of comments\n",
        "            total_comments = len(comments)\n",
        "\n",
        "            video_details.append({\n",
        "                'Title': title,\n",
        "                'Tags': tags,\n",
        "                'Likes': likes,\n",
        "                'Dislikes': dislikes,\n",
        "                'TotalDurationSeconds': total_duration_seconds,  # Total duration in seconds\n",
        "                'Views': views,\n",
        "                'TotalComments': total_comments,\n",
        "                'Comments': comments\n",
        "            })\n",
        "        else:\n",
        "            print(f'No video details found for video ID: {video_id}')\n",
        "    except Exception as e:\n",
        "        print(f'Error processing video ID {video_id}: {str(e)}')\n",
        "\n",
        "# Print the extracted video details\n",
        "for video in video_details:\n",
        "    print(f'Title: {video[\"Title\"]}')\n",
        "    print(f'Tags: {\", \".join(video[\"Tags\"])}')\n",
        "    print(f'Views: {video[\"Views\"]}')\n",
        "    print(f'Likes: {video[\"Likes\"]}')\n",
        "    print(f'Dislikes: {video[\"Dislikes\"]}')\n",
        "    print(f'Total Duration (Seconds): {video[\"TotalDurationSeconds\"]}')  # Print total duration in seconds\n",
        "    print(f'Total Comments: {video[\"TotalComments\"]}')\n",
        "#     print(f'Comments:')\n",
        "#     for comment in video['Comments']:\n",
        "#         print(comment)\n",
        "    print('\\n\\n\\n')\n",
        "    print(\"____________________________________________________________________________________________\")\n",
        "\n",
        "# print(video_details)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kIZPIsHixZoy",
        "outputId": "4dc4394d-63c5-4291-f759-11a15984e963"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Title: Merge Sort | For Beginners | Java Placement Course\n",
            "Tags: C++, C++ coding, C++ full course, C++ placement course, how to code, programming, college placement course, C++ language, java, java language, java full course, java placement, java code, java coding\n",
            "Views: 492217\n",
            "Likes: 8914\n",
            "Dislikes: 0\n",
            "Total Duration (Seconds): 1285\n",
            "Total Comments: 245\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "____________________________________________________________________________________________\n",
            "Title: Merge Sort Algorithm in Java - Full Tutorial with Source\n",
            "Tags: java merge sort, merge sort in java, merge sort in java using recursion, merge sort in java code, merge sort algorithm java, merge sort algorithm, java merge sort algorithm, analysis of mergesort, merge sort, recursive mergesort, java recursive mergesort, java mergesort recursive, codingwithjohn, coding with john, computer science, programming, java, java sorting algorithms, java sorting algorithms examples\n",
            "Views: 143173\n",
            "Likes: 5619\n",
            "Dislikes: 0\n",
            "Total Duration (Seconds): 1382\n",
            "Total Comments: 244\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "____________________________________________________________________________________________\n",
            "Title: Learn Merge Sort in 13 minutes 🔪\n",
            "Tags: Merge sort, merge, merge sorting, merge sort algorithm, recursive sort, recursion, mergesort\n",
            "Views: 167082\n",
            "Likes: 5509\n",
            "Dislikes: 0\n",
            "Total Duration (Seconds): 825\n",
            "Total Comments: 131\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "____________________________________________________________________________________________\n",
            "Title: 2.7.2.  Merge Sort Algorithm\n",
            "Tags: algorithm, algorithms, mergesort, merge sort, recursive merge sort, recursive mergesort, analysis of mergesort, drawbacks of mrgesort, abdul bari, bari, gate\n",
            "Views: 1495730\n",
            "Likes: 21988\n",
            "Dislikes: 0\n",
            "Total Duration (Seconds): 1447\n",
            "Total Comments: 466\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "____________________________________________________________________________________________\n",
            "Title: Merge Sort Using Recursion (Theory + Complexity + Code)\n",
            "Tags: merge sort algorithm, merge sort in data structure, merge sort in java, merge sort java, merge sort, merge sort time complexity, merge sort animation, Merge Sort | Complete explanation for Beginners and Code, merge sort algorithm with java, merge sort example, merge algorithm in merge sort, merge sort complexity analysis, recursion playlist, recursion merge sort, recursion, merge sort code, merge sort tutorial, recursion in java, merge sort in place\n",
            "Views: 164431\n",
            "Likes: 3808\n",
            "Dislikes: 0\n",
            "Total Duration (Seconds): 2987\n",
            "Total Comments: 122\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "____________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CHECKING IF ALL THE VIDEOS ARE OF SAME DOMAIN OR NOT..**"
      ],
      "metadata": {
        "id": "gd8LM3oN6Cxt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#taking the TITLES of the youtube videos\n",
        "\n",
        "titles=[]\n",
        "for i in video_details:\n",
        "    titles.append(i['Title'])\n",
        "\n",
        "\n",
        "#taking the TAGS of the youtube videos\n",
        "\n",
        "tags=[]\n",
        "for i in video_details:\n",
        "    a=((i['Tags']))\n",
        "    tags.append(a)\n",
        "\n",
        "\n",
        "#taking the search texts\n",
        "\n",
        "search_txts=[\"merge sort java code explain easy\"]"
      ],
      "metadata": {
        "id": "gaILXXfp6BfQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "def remove_punctuation(text):\n",
        "    # Remove punctuation and convert to lowercase\n",
        "    return text.translate(str.maketrans('', '', string.punctuation)).lower() # parameters are replace,replacewith,delete\n",
        "\n",
        "def remove_stopwords(text):\n",
        "    # Remove stopwords\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    words = nltk.word_tokenize(text)\n",
        "    filtered_words = [word for word in words if word.lower() not in stop_words]\n",
        "    return ' '.join(filtered_words)\n",
        "\n",
        "def lemmatize(text):\n",
        "    # Lemmatization\n",
        "    wl = WordNetLemmatizer()\n",
        "    words = nltk.word_tokenize(text)\n",
        "    lemmatized_words = [wl.lemmatize(word) for word in words]\n",
        "    return ' '.join(lemmatized_words)\n",
        "\n",
        "def preprocess(text_list):\n",
        "    processed_text_list = []\n",
        "    for text in text_list:\n",
        "        text = remove_punctuation(text)\n",
        "        text = remove_stopwords(text)\n",
        "        text = lemmatize(text)\n",
        "\n",
        "        processed_text_list.append(text)\n",
        "\n",
        "\n",
        "    #removing duplicates\n",
        "    final=[]\n",
        "    for processed_text in processed_text_list:\n",
        "        final.append(set(processed_text.split()))\n",
        "\n",
        "    return final\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scFKC_uFx6tF",
        "outputId": "a91ef2ce-95e9-47c3-918f-ee108ba5aee4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import string\n",
        "\n",
        "# Initialize NLTK's WordNet Lemmatizer and set of English stopwords\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "# Define a function to preprocess and lemmatize a set of strings\n",
        "def preprocess_and_lemmatize(input_set):\n",
        "    processed_set = set()\n",
        "\n",
        "    for input_string in input_set:\n",
        "        # Tokenize the string into words\n",
        "        words = word_tokenize(input_string)\n",
        "\n",
        "        # Remove punctuation and convert to lowercase\n",
        "        words = [word.lower() for word in words if word not in string.punctuation]\n",
        "\n",
        "        # Remove stopwords and lemmatize words\n",
        "        words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]\n",
        "\n",
        "        # Remove empty strings and single-letter words\n",
        "        words = [word for word in words if len(word) > 1]\n",
        "\n",
        "        # Join the words back into a string\n",
        "        processed_string = ' '.join(words)\n",
        "\n",
        "        if processed_string:\n",
        "            processed_set.add(processed_string)\n",
        "\n",
        "    return processed_set\n"
      ],
      "metadata": {
        "id": "A5kOwS82x-Cz"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "\n",
        "# Load the spaCy model (e.g., en_core_web_md for medium-sized word vectors)\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def calculate_similarity(text1, text2):\n",
        "    doc1 = nlp(text1)\n",
        "    doc2 = nlp(text2)\n",
        "    similarity_score = doc1.similarity(doc2)\n",
        "    return similarity_score\n",
        "\n",
        "def find_similaity(list_of_strings):\n",
        "    similarities = []\n",
        "    for i in range(len(list_of_strings)):\n",
        "        for j in range(i + 1, len(list_of_strings)):\n",
        "            similarity = calculate_similarity(list_of_strings[i], list_of_strings[j])\n",
        "            similarities.append(similarity)\n",
        "\n",
        "#     print(similarities)\n",
        "    avg=sum(similarities) /len(similarities)\n",
        "\n",
        "    return avg"
      ],
      "metadata": {
        "id": "lvvahNQhyBqS"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "# Ignore all warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "\n",
        "if(len(search_txts)==1):\n",
        "\n",
        "    # finding if the TITLES are similar or not  by NLTK NLP\n",
        "\n",
        "    processed_titles = preprocess(titles)\n",
        "    final_titles = set.intersection(*processed_titles)\n",
        "\n",
        "    if(len(final_titles)>0):\n",
        "\n",
        "        #finding if the TAGS are similar or not  by NLTK NLP\n",
        "\n",
        "        data = tags\n",
        "        # Split each set into individual words\n",
        "        split_sets = []\n",
        "        for s in data:\n",
        "            split_words = set()\n",
        "            for item in s:\n",
        "                split_words.update(item.split())\n",
        "            split_sets.append(split_words)\n",
        "\n",
        "        preprocessed_data = [preprocess_and_lemmatize(split_set) for split_set in split_sets]\n",
        "        final_tags=set.intersection(*preprocessed_data)\n",
        "\n",
        "\n",
        "        if(len(final_tags)>0):\n",
        "            print(\"videos belong to same domain --- by NLTK NLP\")\n",
        "            flag1=True\n",
        "        else:\n",
        "            print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "            flag1=False\n",
        "\n",
        "    else:\n",
        "        print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "        flag1=False\n",
        "\n",
        "    print(\"_________________________________________________________________\")\n",
        "\n",
        "    # finding if the TITLES are similar or not  by SPACY\n",
        "    titles_sim_score=find_similaity(titles)\n",
        "    print(\"TITLES SIMILARITY SCORE: \",titles_sim_score)\n",
        "\n",
        "    if(titles_sim_score>0.4):\n",
        "        print(\"videos belong to same domain --- by SPACY NLP -- both above threshold\")\n",
        "        flag2=True\n",
        "\n",
        "    else:\n",
        "        print(\"videos does not belong to same domain --- by SPACY NLP --  below threshold\")\n",
        "        flag2=False\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "else:\n",
        "\n",
        "    # finding if the TITLES are similar or not  by NLTK NLP\n",
        "    processed_titles = preprocess(titles)\n",
        "    final_titles = set.intersection(*processed_titles)\n",
        "\n",
        "    if(len(final_titles)>0):\n",
        "\n",
        "        #finding if the TAGS are similar or not  by NLTK NLP\n",
        "\n",
        "        data = tags\n",
        "        # Split each set into individual words\n",
        "        split_sets = []\n",
        "        for s in data:\n",
        "            split_words = set()\n",
        "            for item in s:\n",
        "                split_words.update(item.split())\n",
        "            split_sets.append(split_words)\n",
        "\n",
        "        preprocessed_data = [preprocess_and_lemmatize(split_set) for split_set in split_sets]\n",
        "        final_tags=set.intersection(*preprocessed_data)\n",
        "\n",
        "        #combining both titles and tags after all processings inorder to check with search_texts\n",
        "        # as the titles and tags determine the search text\n",
        "\n",
        "        to_search=set(list(final_titles)+list(final_tags))\n",
        "\n",
        "        if(len(final_tags)>0):\n",
        "\n",
        "            #check if SEARCH TEXTS of videos are similar\n",
        "            a=preprocess(search_txts)\n",
        "\n",
        "            n=[]\n",
        "            for i in a:\n",
        "                n.append(set(word for word in i if len(word) > 1))\n",
        "\n",
        "            final_search_txts=set.intersection(*n)\n",
        "\n",
        "            if(len(final_search_txts)>0):\n",
        "                finall=final_search_txts.intersection(to_search)\n",
        "\n",
        "                if(len(finall)>0):\n",
        "                    print(\"videos belong to same domain --- by NLTK NLP\")\n",
        "                    flag1=True\n",
        "                else:\n",
        "                    print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "                    flag1=False\n",
        "\n",
        "            else:\n",
        "                print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "                flag1=False\n",
        "\n",
        "\n",
        "        else:\n",
        "            print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "            flag1=False\n",
        "\n",
        "    else:\n",
        "        print(\"videos does not belong to same domain --- by NLTK NLP\")\n",
        "        flag1=False\n",
        "\n",
        "    print(\"_________________________________________________________________\")\n",
        "\n",
        "    # finding if the SEARCH TEXTS are similar or not by SPACY\n",
        "    search_sim_score=find_similaity(search_txts)\n",
        "    print(\"SEARCH SIMILARITY SCORE: \",search_sim_score)\n",
        "\n",
        "    # finding if the TITLES are similar or not  by SPACY\n",
        "    titles_sim_score=find_similaity(titles)\n",
        "    print(\"TITLES SIMILARITY SCORE: \",titles_sim_score)\n",
        "\n",
        "    if(search_sim_score>0.4 and titles_sim_score>0.4):\n",
        "        print(\"videos belong to same domain --- by SPACY NLP -- both above threshold\")\n",
        "        flag2=True\n",
        "\n",
        "    else:\n",
        "        print(\"videos does not belong to same domain --- by SPACY NLP --  below threshold\")\n",
        "        flag2=False\n",
        "\n",
        "\n",
        "print(\"_________________________________________________________________\")\n",
        "print(\"_________________________________________________________________\")\n",
        "if(flag1==True and flag2==True):\n",
        "    print(\"VIDEOS BELONG TO SAME DOMAIN --- can do NLP analysis on COMMENTS\")\n",
        "\n",
        "else:\n",
        "    print(\"take videos of same domain only\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC64sXGfyB7N",
        "outputId": "1545983f-2001-4d18-ede6-72be917ccd0d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "videos does not belong to same domain --- by NLTK NLP\n",
            "_________________________________________________________________\n",
            "TITLES SIMILARITY SCORE:  0.556801118806064\n",
            "videos belong to same domain --- by SPACY NLP -- both above threshold\n",
            "_________________________________________________________________\n",
            "_________________________________________________________________\n",
            "take videos of same domain only\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " NLP ANALYSIS ON COMMENTS OF ALL VIDEOS"
      ],
      "metadata": {
        "id": "TPwZrZxp60CM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load the slang_words json for SLANG WORDS handling\n",
        "import json\n",
        "\n",
        "with open('/content/drive/MyDrive/yt/slang_words.json', 'r') as file:\n",
        "    slang_words = json.load(file)\n",
        "\n",
        "# load the language-code json for further handling\n",
        "with open('/content/drive/MyDrive/yt/lang_code_fullname.json', 'r') as file:\n",
        "    lang_code_fullname = json.load(file)\n",
        "\n",
        "print(\"loaded..\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFgB9yG751lq",
        "outputId": "b9e81961-ca7f-47b0-d543-2262ddde07f9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loaded..\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#taking the COMMENTS of the youtube videos\n",
        "\n",
        "comms=[]\n",
        "for i in video_details:\n",
        "    comms.append(i['Comments'])\n",
        "for j in comms:\n",
        "    print(len(j))\n",
        "print(\"no. of videos : \",len(comms))\n",
        "# comms\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiBSwIi6ylty",
        "outputId": "e18eadf1-9ed6-4db5-c2da-12e8da858970"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "245\n",
            "244\n",
            "131\n",
            "466\n",
            "122\n",
            "no. of videos :  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#storing comments of each videos into a pandas Dataframe\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming you have a list of lists containing comments (comments_list)\n",
        "comments_list = comms\n",
        "\n",
        "# Define a list of names for the DataFrames\n",
        "video_names = [\"video_%d\" % (i+1) for i in range(len(comments_list))]\n",
        "\n",
        "# Create a dictionary to store DataFrames\n",
        "video_dataframes = {}\n",
        "\n",
        "# Iterate through comments and video names\n",
        "for comments, video_name in zip(comments_list, video_names):\n",
        "    # Create a DataFrame for each video with 'Comments' as the column name\n",
        "    df = pd.DataFrame({\"Comments\": comments})\n",
        "    video_dataframes[video_name] = df\n",
        "\n",
        "\n",
        "print(video_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWds99L3y32k",
        "outputId": "73f9ebad-f8fb-435a-8575-e7550351cb96"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['video_1', 'video_2', 'video_3', 'video_4', 'video_5']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from bs4 import BeautifulSoup\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import nltk\n",
        "from textblob import TextBlob\n",
        "import contractions\n",
        "\n",
        "\n",
        "# Download necessary resources\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Instantiate lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "# Assuming you have a set of stop words\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "def preprocess_text(review):\n",
        "\n",
        "    #replacing slang words with their fullforms\n",
        "    words = review.split()\n",
        "    for word in words:\n",
        "        if word in slang_words:\n",
        "            review = review.replace(word, slang_words[word])\n",
        "\n",
        "    review = re.sub(r\"http\\S+\", \"\", review)             # removing website links\n",
        "    review = re.sub(r'@[\\w]+', ' ', review)\n",
        "    review = BeautifulSoup(review, 'lxml').get_text()   # removing html tags\n",
        "    review = contractions.fix(review)                       # Expand contractions\n",
        "    review = re.sub(r'\\d+',\"\", review).strip()   # removing the words with numeric digits\n",
        "    review = re.sub('[^A-Za-z]+', ' ', review)          # removing non-word characters\n",
        "    #take out emojis\n",
        "    review = re.sub(r'[\\U0001F600-\\U0001F64F\\U0001F300-\\U0001F5FF\\U0001F680-\\U0001F6FF\\U0001F700-\\U0001F77F\\U0001F780-\\U0001F7FF\\U0001F800-\\U0001F8FF\\U0001F900-\\U0001F9FF\\U0001FA00-\\U0001FA6F\\U0001FA70-\\U0001FAFF\\U0001F004-\\U0001F0CF\\U0001F170-\\U0001F251\\U0001F004-\\U0001F0CF\\U0001F300-\\U0001F5FF\\U0001F600-\\U0001F64F\\U0001F680-\\U0001F6FF\\U0001F700-\\U0001F77F\\U0001F780-\\U0001F7FF\\U0001F800-\\U0001F8FF\\U0001F900-\\U0001F9FF\\U0001FA00-\\U0001FA6F\\U0001FA70-\\U0001FAFF\\U0001F004-\\U0001F0CF\\U0001F170-\\U0001F251]', '', review)\n",
        "    review = review.lower()                             # converting to lower case\n",
        "    # review=(str(TextBlob(review).correct()))   #correct spelling mistakes/grammer mistakes\n",
        "    # review = [word for word in review.split(\" \") if not word in stop_words] # removing stop words\n",
        "    review = [lemmatizer.lemmatize(token, \"v\") for token in review] # Lemmatization\n",
        "    review = \"\".join(review)\n",
        "    review.strip()\n",
        "    return review\n",
        "\n",
        "\n",
        "#SAMPLE TEST\n",
        "\n",
        "# Example usage:\n",
        "input_text = \"😋 #FF Thiss is an exampl of a running with som spellling mistakss. I can't believe it! This is an @example sentence with some http://example.com links and <html> tags. It also has 123 numbers. @anil thiss\"\n",
        "\n",
        "preprocessed_text = preprocess_text(input_text)\n",
        "print(\"Original Text:\", input_text)\n",
        "print(\"Preprocessed Text:\", preprocessed_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61n3IFgS3kvo",
        "outputId": "5f10525a-d23b-443d-c269-11edd9d1976e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Text: 😋 #FF Thiss is an exampl of a running with som spellling mistakss. I can't believe it! This is an @example sentence with some http://example.com links and <html> tags. It also has 123 numbers. @anil thiss\n",
            "Preprocessed Text:  follow friday this is is an exampl of a running with som spellling mistakss i cannot believe it this is an sentence with some links and tags it also has it means i agree numbers this is\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dealing with comments of all languages\n",
        "\n",
        "\n",
        "from googletrans import Translator\n",
        "from indic_transliteration import sanscript\n",
        "from indic_transliteration.sanscript import transliterate\n",
        "import pycld2 as cld2\n",
        "import pandas as pd\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import words\n",
        "\n",
        "nltk.download('words')\n",
        "english_word_set = set(words.words())\n",
        "\n",
        "def is_english_word(word):\n",
        "    return word.lower() in english_word_set\n",
        "\n",
        "# Function to remove punctuation and split the sentence into words\n",
        "def process_sentence(sentence):\n",
        "    sentence = sentence.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
        "    words = sentence.split()\n",
        "    return words\n",
        "\n",
        "# Function to detect language using cld2.detect\n",
        "def detect_language(text):\n",
        "    _, _, _, detected_languages = cld2.detect(text, returnVectors=True)\n",
        "    return detected_languages\n",
        "\n",
        "# Function to detect language for each word in a sentence\n",
        "def detect_languages_for_words(words):\n",
        "    word_language_dict = {}\n",
        "\n",
        "    for word in words:\n",
        "      if(is_english_word(word)==True):\n",
        "          word_language_dict[word] = \"ENGLISH\"\n",
        "      else:\n",
        "        language = detect_language(word)\n",
        "        word_language_dict[word] = language[0][2]\n",
        "    return word_language_dict\n",
        "\n",
        "# Function to determine the type of sentence\n",
        "def determine_sentence_type(detected_languages):\n",
        "    if len(detected_languages) == 1:\n",
        "        return detected_languages[0][2]\n",
        "    elif len(detected_languages) > 1:\n",
        "        return \"mixed\"\n",
        "    else:\n",
        "        return \"unknown\"\n",
        "\n",
        "# Language detection function using googletrans\n",
        "def detect_language_googletrans(text):\n",
        "    translator = Translator()\n",
        "    detected_lang = translator.detect(text)\n",
        "\n",
        "    try:\n",
        "        return lang_code_fullname.get(detected_lang.lang)\n",
        "    except Exception as e:\n",
        "        print(f\"Error detecting language: {e}\")\n",
        "        return \"unknown\"\n",
        "\n",
        "# Transliterate words based on detected language\n",
        "def transliterate_words(words, detected_lang):\n",
        "    script_constant = sanscript.DEVANAGARI if detected_lang.upper() == \"HINDI\" else getattr(sanscript, detected_lang.upper(), sanscript.ITRANS)\n",
        "    return [transliterate(word, sanscript.ITRANS, script_constant) for word in words]\n",
        "\n",
        "# Reverse the dictionary\n",
        "lang_name_to_code = {v: k for k, v in lang_code_fullname.items()}\n",
        "\n",
        "def get_language_code(language_name):\n",
        "    return lang_name_to_code.get(language_name)\n",
        "\n",
        "# Translate mixed-language sentences to English\n",
        "def translate_to_english(sentence):\n",
        "    translator = Translator()\n",
        "    language = detect_language_googletrans(sentence)\n",
        "    # Reverse the dictionary\n",
        "    lang_name_to_code = {v: k for k, v in lang_code_fullname.items()}\n",
        "    lang = get_language_code(language)\n",
        "    a=translator.translate(sentence,src=lang, dest='en').text\n",
        "    return a\n",
        "\n",
        "\n",
        "# delete \"unknown\" words in \"mixed\" type\n",
        "def delete_pair_by_value(my_dict, value_to_delete):\n",
        "    keys_to_delete = [key for key, value in my_dict.items() if value == value_to_delete]\n",
        "\n",
        "    for key in keys_to_delete:\n",
        "        del my_dict[key]\n",
        "\n",
        "\n",
        "def translate_to_english1(sentence, source_language='auto', target_language='en'):\n",
        "    if sentence is None or not sentence.strip():\n",
        "        return \"\"\n",
        "\n",
        "    translator = Translator()\n",
        "    translation = translator.translate(sentence, src=source_language, dest=target_language)\n",
        "    return translation.text\n",
        "\n",
        "\n",
        "\n",
        "# SAMPLE TEST\n",
        "\n",
        "sentences = [\n",
        "    \"nenu unnanu. वह अपने घर जा रहा है.\",\n",
        "    \"The quick brown fox jumps over the lazy dog.\",\n",
        "    \"త్వరలు ముద్దు కింద ఉంటుంది.\",\n",
        "    \"kitaab duniya\",\n",
        "    \"तेज भूरी लोमड़ी सुसु करती है आलसी कुत्ते के ऊपर.\",\n",
        "    \"The sun is shining brightly in the sky.\",\n",
        "    \"పూర్వోత్తరం వేసిన సూర్యుడు ప్రజలను అభివాదిస్తున్నాడు.\",\n",
        "    \"सूरज आसमान में चमक रहा है.\",\n",
        "    \"आलसी कुत्ते nenu unnanu ఫ్రీ టైమ్‌లో \",\n",
        "    \"I love reading books in my free time.\",\n",
        "    \"నాకు ఫ్రీ టైమ్‌లో పుస్తకాలను చదివడం ఇష్టం.\",\n",
        "    \"मुझे फुर्सत के समय में पुस्तकें पढ़ना पसंद है.\",\n",
        "    \"Please pass me the salt and pepper.\",\n",
        "    \" Nenu akkada unnaanu\",\n",
        "    \"Today is a lovely day. తన ఇంటికి వెళ్తున్నాడు.\",\n",
        "    \"పూర్వోత్తరం सूरज\"\n",
        "]\n",
        "\n",
        "\n",
        "data = []\n",
        "for sentence in sentences:\n",
        "    words = process_sentence(sentence)\n",
        "    detected_languages = detect_language(sentence)\n",
        "    word_language_dict = detect_languages_for_words(words)\n",
        "    sent_type = determine_sentence_type(detected_languages)\n",
        "\n",
        "    if sent_type == \"Unknown\":\n",
        "        detected_lang_googletrans = detect_language_googletrans(sentence)\n",
        "        words_transliterated = transliterate_words(words, detected_lang_googletrans.upper())\n",
        "\n",
        "\n",
        "        sentence_data = {\n",
        "            'Sentence': sentence,\n",
        "            'Words': words,\n",
        "            'Detected Languages': detected_languages,\n",
        "            'Type': sent_type,\n",
        "            'Detected Language (Each Word)': word_language_dict,\n",
        "            'Detected Language (Googletrans)': detected_lang_googletrans,\n",
        "            'Transliterated Words': ' '.join(words_transliterated),\n",
        "            'Final Sentence': ' '.join(words_transliterated),  # Use Transliterated Words for Unknown type\n",
        "            'FINALLL': translate_to_english1(' '.join(words_transliterated))\n",
        "        }\n",
        "    elif sent_type == \"mixed\":\n",
        "      value_to_delete = \"Unknown\"\n",
        "\n",
        "      delete_pair_by_value(word_language_dict, value_to_delete)\n",
        "\n",
        "      # Create a sentence with space between each word\n",
        "      sentence1 = ' '.join(word_language_dict.keys())\n",
        "\n",
        "      english_translation = translate_to_english(sentence1)\n",
        "\n",
        "      sentence_data = {\n",
        "          'Sentence': sentence,\n",
        "          'Words': words,\n",
        "          'Detected Languages': detected_languages,\n",
        "          'Type': sent_type,\n",
        "          'Detected Language (Each Word)': word_language_dict,\n",
        "          'Translated to English': english_translation,\n",
        "          'Final Sentence': english_translation,  # Use Translated to English for Mixed type\n",
        "          'FINALLL':  translate_to_english1(english_translation)\n",
        "      }\n",
        "    else:\n",
        "        sentence_data = {\n",
        "            'Sentence': sentence,\n",
        "            'Words': words,\n",
        "            'Detected Languages': detected_languages,\n",
        "            'Type': sent_type,\n",
        "            'Detected Language (Each Word)': word_language_dict,\n",
        "            'Final Sentence': sentence,  # Use Sentence for other types\n",
        "            'FINALLL': translate_to_english1(sentence)\n",
        "        }\n",
        "\n",
        "    data.append(sentence_data)\n",
        "\n",
        "# Create a DataFrame\n",
        "result_df = pd.DataFrame(data)\n",
        "\n",
        "# Reorder columns to have \"Final Sentence\" at the end\n",
        "column_order = ['Sentence', 'Words', 'Detected Languages', 'Type', 'Detected Language (Each Word)',\n",
        "                 'Detected Language (Googletrans)', 'Transliterated Words', 'Translated to English', 'Final Sentence','FINALLL']\n",
        "\n",
        "result_df = result_df[column_order]\n",
        "result_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5RQCX71X5N_L",
        "outputId": "528e37bd-16f2-43ae-cb54-d176240ab10a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package words to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/words.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             Sentence  \\\n",
              "0                  nenu unnanu. वह अपने घर जा रहा है.   \n",
              "1        The quick brown fox jumps over the lazy dog.   \n",
              "2                         త్వరలు ముద్దు కింద ఉంటుంది.   \n",
              "3                                       kitaab duniya   \n",
              "4    तेज भूरी लोमड़ी सुसु करती है आलसी कुत्ते के ऊपर.   \n",
              "5             The sun is shining brightly in the sky.   \n",
              "6   పూర్వోత్తరం వేసిన సూర్యుడు ప్రజలను అభివాదిస్తు...   \n",
              "7                          सूरज आसमान में चमक रहा है.   \n",
              "8               आलसी कुत्ते nenu unnanu ఫ్రీ టైమ్‌లో    \n",
              "9               I love reading books in my free time.   \n",
              "10         నాకు ఫ్రీ టైమ్‌లో పుస్తకాలను చదివడం ఇష్టం.   \n",
              "11     मुझे फुर्सत के समय में पुस्तकें पढ़ना पसंद है.   \n",
              "12                Please pass me the salt and pepper.   \n",
              "13                                Nenu akkada unnaanu   \n",
              "14     Today is a lovely day. తన ఇంటికి వెళ్తున్నాడు.   \n",
              "15                                   పూర్వోత్తరం सूरज   \n",
              "\n",
              "                                                Words  \\\n",
              "0           [nenu, unnanu, वह, अपने, घर, जा, रहा, है]   \n",
              "1   [The, quick, brown, fox, jumps, over, the, laz...   \n",
              "2                     [త్వరలు, ముద్దు, కింద, ఉంటుంది]   \n",
              "3                                    [kitaab, duniya]   \n",
              "4   [तेज, भूरी, लोमड़ी, सुसु, करती, है, आलसी, कुत्...   \n",
              "5     [The, sun, is, shining, brightly, in, the, sky]   \n",
              "6   [పూర్వోత్తరం, వేసిన, సూర్యుడు, ప్రజలను, అభివాద...   \n",
              "7                    [सूरज, आसमान, में, चमक, रहा, है]   \n",
              "8         [आलसी, कुत्ते, nenu, unnanu, ఫ్రీ, టైమ్‌లో]   \n",
              "9       [I, love, reading, books, in, my, free, time]   \n",
              "10   [నాకు, ఫ్రీ, టైమ్‌లో, పుస్తకాలను, చదివడం, ఇష్టం]   \n",
              "11  [मुझे, फुर्सत, के, समय, में, पुस्तकें, पढ़ना, ...   \n",
              "12         [Please, pass, me, the, salt, and, pepper]   \n",
              "13                            [Nenu, akkada, unnaanu]   \n",
              "14  [Today, is, a, lovely, day, తన, ఇంటికి, వెళ్తు...   \n",
              "15                                [పూర్వోత్తరం, सूरज]   \n",
              "\n",
              "                              Detected Languages     Type  \\\n",
              "0    ((0, 13, Unknown, un), (13, 51, HINDI, hi))    mixed   \n",
              "1                        ((0, 44, ENGLISH, en),)  ENGLISH   \n",
              "2                         ((0, 73, TELUGU, te),)   TELUGU   \n",
              "3                        ((0, 13, Unknown, un),)  Unknown   \n",
              "4                         ((0, 124, HINDI, hi),)    HINDI   \n",
              "5                        ((0, 39, ENGLISH, en),)  ENGLISH   \n",
              "6                        ((0, 149, TELUGU, te),)   TELUGU   \n",
              "7                          ((0, 66, HINDI, hi),)    HINDI   \n",
              "8   ((0, 44, Unknown, un), (44, 35, TELUGU, te))    mixed   \n",
              "9                        ((0, 37, ENGLISH, en),)  ENGLISH   \n",
              "10                       ((0, 114, TELUGU, te),)   TELUGU   \n",
              "11                        ((0, 120, HINDI, hi),)    HINDI   \n",
              "12                       ((0, 35, ENGLISH, en),)  ENGLISH   \n",
              "13                       ((0, 20, Unknown, un),)  Unknown   \n",
              "14  ((0, 23, ENGLISH, en), (23, 63, TELUGU, te))    mixed   \n",
              "15    ((0, 34, TELUGU, te), (34, 12, HINDI, hi))    mixed   \n",
              "\n",
              "                        Detected Language (Each Word)  \\\n",
              "0                      {'घर': 'HINDI', 'जा': 'HINDI'}   \n",
              "1   {'The': 'ENGLISH', 'quick': 'ENGLISH', 'brown'...   \n",
              "2   {'త్వరలు': 'TELUGU', 'ముద్దు': 'TELUGU', 'కింద...   \n",
              "3          {'kitaab': 'Unknown', 'duniya': 'Unknown'}   \n",
              "4   {'तेज': 'HINDI', 'भूरी': 'Unknown', 'लोमड़ी': ...   \n",
              "5   {'The': 'ENGLISH', 'sun': 'ENGLISH', 'is': 'EN...   \n",
              "6   {'పూర్వోత్తరం': 'TELUGU', 'వేసిన': 'TELUGU', '...   \n",
              "7   {'सूरज': 'HINDI', 'आसमान': 'HINDI', 'में': 'HI...   \n",
              "8             {'ఫ్రీ': 'TELUGU', 'టైమ్‌లో': 'TELUGU'}   \n",
              "9   {'I': 'ENGLISH', 'love': 'ENGLISH', 'reading':...   \n",
              "10  {'నాకు': 'TELUGU', 'ఫ్రీ': 'TELUGU', 'టైమ్‌లో'...   \n",
              "11  {'मुझे': 'Unknown', 'फुर्सत': 'HINDI', 'के': '...   \n",
              "12  {'Please': 'ENGLISH', 'pass': 'ENGLISH', 'me':...   \n",
              "13  {'Nenu': 'Unknown', 'akkada': 'Unknown', 'unna...   \n",
              "14  {'Today': 'ENGLISH', 'is': 'ENGLISH', 'a': 'EN...   \n",
              "15         {'పూర్వోత్తరం': 'TELUGU', 'सूरज': 'HINDI'}   \n",
              "\n",
              "   Detected Language (Googletrans) Transliterated Words  \\\n",
              "0                              NaN                  NaN   \n",
              "1                              NaN                  NaN   \n",
              "2                              NaN                  NaN   \n",
              "3                            Hindi         किताब् दुनिय   \n",
              "4                              NaN                  NaN   \n",
              "5                              NaN                  NaN   \n",
              "6                              NaN                  NaN   \n",
              "7                              NaN                  NaN   \n",
              "8                              NaN                  NaN   \n",
              "9                              NaN                  NaN   \n",
              "10                             NaN                  NaN   \n",
              "11                             NaN                  NaN   \n",
              "12                             NaN                  NaN   \n",
              "13                          Telugu   ణేను అక్కద ఉన్నాను   \n",
              "14                             NaN                  NaN   \n",
              "15                             NaN                  NaN   \n",
              "\n",
              "                         Translated to English  \\\n",
              "0                                   Go to home   \n",
              "1                                          NaN   \n",
              "2                                          NaN   \n",
              "3                                          NaN   \n",
              "4                                          NaN   \n",
              "5                                          NaN   \n",
              "6                                          NaN   \n",
              "7                                          NaN   \n",
              "8                                 At free time   \n",
              "9                                          NaN   \n",
              "10                                         NaN   \n",
              "11                                         NaN   \n",
              "12                                         NaN   \n",
              "13                                         NaN   \n",
              "14  Today is going to his house on a lvely day   \n",
              "15                            Preforation सूरज   \n",
              "\n",
              "                                       Final Sentence  \\\n",
              "0                                          Go to home   \n",
              "1        The quick brown fox jumps over the lazy dog.   \n",
              "2                         త్వరలు ముద్దు కింద ఉంటుంది.   \n",
              "3                                        किताब् दुनिय   \n",
              "4    तेज भूरी लोमड़ी सुसु करती है आलसी कुत्ते के ऊपर.   \n",
              "5             The sun is shining brightly in the sky.   \n",
              "6   పూర్వోత్తరం వేసిన సూర్యుడు ప్రజలను అభివాదిస్తు...   \n",
              "7                          सूरज आसमान में चमक रहा है.   \n",
              "8                                        At free time   \n",
              "9               I love reading books in my free time.   \n",
              "10         నాకు ఫ్రీ టైమ్‌లో పుస్తకాలను చదివడం ఇష్టం.   \n",
              "11     मुझे फुर्सत के समय में पुस्तकें पढ़ना पसंद है.   \n",
              "12                Please pass me the salt and pepper.   \n",
              "13                                 ణేను అక్కద ఉన్నాను   \n",
              "14         Today is going to his house on a lvely day   \n",
              "15                                   Preforation सूरज   \n",
              "\n",
              "                                         FINALLL  \n",
              "0                                     Go to home  \n",
              "1   The quick brown fox jumps over the lazy dog.  \n",
              "2              Soon the kiss will be underneath.  \n",
              "3                              What is the world  \n",
              "4   The sharp brown fox smooths on the lazy dog.  \n",
              "5        The sun is shining brightly in the sky.  \n",
              "6      The sun has been forced to be the people.  \n",
              "7                 The sun is shining in the sky.  \n",
              "8                                   At free time  \n",
              "9          I love reading books in my free time.  \n",
              "10            I like reading books at free time.  \n",
              "11     I like to read books during leisure time.  \n",
              "12           Please pass me the salt and pepper.  \n",
              "13                                    I am there  \n",
              "14   Today is going to his house on a lovely day  \n",
              "15                               Preforation sun  "
            ],
            "text/html": [
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              "    <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sentence</th>\n",
              "      <th>Words</th>\n",
              "      <th>Detected Languages</th>\n",
              "      <th>Type</th>\n",
              "      <th>Detected Language (Each Word)</th>\n",
              "      <th>Detected Language (Googletrans)</th>\n",
              "      <th>Transliterated Words</th>\n",
              "      <th>Translated to English</th>\n",
              "      <th>Final Sentence</th>\n",
              "      <th>FINALLL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nenu unnanu. वह अपने घर जा रहा है.</td>\n",
              "      <td>[nenu, unnanu, वह, अपने, घर, जा, रहा, है]</td>\n",
              "      <td>((0, 13, Unknown, un), (13, 51, HINDI, hi))</td>\n",
              "      <td>mixed</td>\n",
              "      <td>{'घर': 'HINDI', 'जा': 'HINDI'}</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Go to home</td>\n",
              "      <td>Go to home</td>\n",
              "      <td>Go to home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "      <td>[The, quick, brown, fox, jumps, over, the, laz...</td>\n",
              "      <td>((0, 44, ENGLISH, en),)</td>\n",
              "      <td>ENGLISH</td>\n",
              "      <td>{'The': 'ENGLISH', 'quick': 'ENGLISH', 'brown'...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "      <td>The quick brown fox jumps over the lazy dog.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>త్వరలు ముద్దు కింద ఉంటుంది.</td>\n",
              "      <td>[త్వరలు, ముద్దు, కింద, ఉంటుంది]</td>\n",
              "      <td>((0, 73, TELUGU, te),)</td>\n",
              "      <td>TELUGU</td>\n",
              "      <td>{'త్వరలు': 'TELUGU', 'ముద్దు': 'TELUGU', 'కింద...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>త్వరలు ముద్దు కింద ఉంటుంది.</td>\n",
              "      <td>Soon the kiss will be underneath.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>kitaab duniya</td>\n",
              "      <td>[kitaab, duniya]</td>\n",
              "      <td>((0, 13, Unknown, un),)</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>{'kitaab': 'Unknown', 'duniya': 'Unknown'}</td>\n",
              "      <td>Hindi</td>\n",
              "      <td>किताब् दुनिय</td>\n",
              "      <td>NaN</td>\n",
              "      <td>किताब् दुनिय</td>\n",
              "      <td>What is the world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>तेज भूरी लोमड़ी सुसु करती है आलसी कुत्ते के ऊपर.</td>\n",
              "      <td>[तेज, भूरी, लोमड़ी, सुसु, करती, है, आलसी, कुत्...</td>\n",
              "      <td>((0, 124, HINDI, hi),)</td>\n",
              "      <td>HINDI</td>\n",
              "      <td>{'तेज': 'HINDI', 'भूरी': 'Unknown', 'लोमड़ी': ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>तेज भूरी लोमड़ी सुसु करती है आलसी कुत्ते के ऊपर.</td>\n",
              "      <td>The sharp brown fox smooths on the lazy dog.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>The sun is shining brightly in the sky.</td>\n",
              "      <td>[The, sun, is, shining, brightly, in, the, sky]</td>\n",
              "      <td>((0, 39, ENGLISH, en),)</td>\n",
              "      <td>ENGLISH</td>\n",
              "      <td>{'The': 'ENGLISH', 'sun': 'ENGLISH', 'is': 'EN...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The sun is shining brightly in the sky.</td>\n",
              "      <td>The sun is shining brightly in the sky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>పూర్వోత్తరం వేసిన సూర్యుడు ప్రజలను అభివాదిస్తు...</td>\n",
              "      <td>[పూర్వోత్తరం, వేసిన, సూర్యుడు, ప్రజలను, అభివాద...</td>\n",
              "      <td>((0, 149, TELUGU, te),)</td>\n",
              "      <td>TELUGU</td>\n",
              "      <td>{'పూర్వోత్తరం': 'TELUGU', 'వేసిన': 'TELUGU', '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>పూర్వోత్తరం వేసిన సూర్యుడు ప్రజలను అభివాదిస్తు...</td>\n",
              "      <td>The sun has been forced to be the people.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>सूरज आसमान में चमक रहा है.</td>\n",
              "      <td>[सूरज, आसमान, में, चमक, रहा, है]</td>\n",
              "      <td>((0, 66, HINDI, hi),)</td>\n",
              "      <td>HINDI</td>\n",
              "      <td>{'सूरज': 'HINDI', 'आसमान': 'HINDI', 'में': 'HI...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>सूरज आसमान में चमक रहा है.</td>\n",
              "      <td>The sun is shining in the sky.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>आलसी कुत्ते nenu unnanu ఫ్రీ టైమ్‌లో</td>\n",
              "      <td>[आलसी, कुत्ते, nenu, unnanu, ఫ్రీ, టైమ్‌లో]</td>\n",
              "      <td>((0, 44, Unknown, un), (44, 35, TELUGU, te))</td>\n",
              "      <td>mixed</td>\n",
              "      <td>{'ఫ్రీ': 'TELUGU', 'టైమ్‌లో': 'TELUGU'}</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>At free time</td>\n",
              "      <td>At free time</td>\n",
              "      <td>At free time</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>I love reading books in my free time.</td>\n",
              "      <td>[I, love, reading, books, in, my, free, time]</td>\n",
              "      <td>((0, 37, ENGLISH, en),)</td>\n",
              "      <td>ENGLISH</td>\n",
              "      <td>{'I': 'ENGLISH', 'love': 'ENGLISH', 'reading':...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I love reading books in my free time.</td>\n",
              "      <td>I love reading books in my free time.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>నాకు ఫ్రీ టైమ్‌లో పుస్తకాలను చదివడం ఇష్టం.</td>\n",
              "      <td>[నాకు, ఫ్రీ, టైమ్‌లో, పుస్తకాలను, చదివడం, ఇష్టం]</td>\n",
              "      <td>((0, 114, TELUGU, te),)</td>\n",
              "      <td>TELUGU</td>\n",
              "      <td>{'నాకు': 'TELUGU', 'ఫ్రీ': 'TELUGU', 'టైమ్‌లో'...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>నాకు ఫ్రీ టైమ్‌లో పుస్తకాలను చదివడం ఇష్టం.</td>\n",
              "      <td>I like reading books at free time.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>मुझे फुर्सत के समय में पुस्तकें पढ़ना पसंद है.</td>\n",
              "      <td>[मुझे, फुर्सत, के, समय, में, पुस्तकें, पढ़ना, ...</td>\n",
              "      <td>((0, 120, HINDI, hi),)</td>\n",
              "      <td>HINDI</td>\n",
              "      <td>{'मुझे': 'Unknown', 'फुर्सत': 'HINDI', 'के': '...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>मुझे फुर्सत के समय में पुस्तकें पढ़ना पसंद है.</td>\n",
              "      <td>I like to read books during leisure time.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Please pass me the salt and pepper.</td>\n",
              "      <td>[Please, pass, me, the, salt, and, pepper]</td>\n",
              "      <td>((0, 35, ENGLISH, en),)</td>\n",
              "      <td>ENGLISH</td>\n",
              "      <td>{'Please': 'ENGLISH', 'pass': 'ENGLISH', 'me':...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Please pass me the salt and pepper.</td>\n",
              "      <td>Please pass me the salt and pepper.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Nenu akkada unnaanu</td>\n",
              "      <td>[Nenu, akkada, unnaanu]</td>\n",
              "      <td>((0, 20, Unknown, un),)</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>{'Nenu': 'Unknown', 'akkada': 'Unknown', 'unna...</td>\n",
              "      <td>Telugu</td>\n",
              "      <td>ణేను అక్కద ఉన్నాను</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ణేను అక్కద ఉన్నాను</td>\n",
              "      <td>I am there</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Today is a lovely day. తన ఇంటికి వెళ్తున్నాడు.</td>\n",
              "      <td>[Today, is, a, lovely, day, తన, ఇంటికి, వెళ్తు...</td>\n",
              "      <td>((0, 23, ENGLISH, en), (23, 63, TELUGU, te))</td>\n",
              "      <td>mixed</td>\n",
              "      <td>{'Today': 'ENGLISH', 'is': 'ENGLISH', 'a': 'EN...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Today is going to his house on a lvely day</td>\n",
              "      <td>Today is going to his house on a lvely day</td>\n",
              "      <td>Today is going to his house on a lovely day</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>పూర్వోత్తరం सूरज</td>\n",
              "      <td>[పూర్వోత్తరం, सूरज]</td>\n",
              "      <td>((0, 34, TELUGU, te), (34, 12, HINDI, hi))</td>\n",
              "      <td>mixed</td>\n",
              "      <td>{'పూర్వోత్తరం': 'TELUGU', 'सूरज': 'HINDI'}</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Preforation सूरज</td>\n",
              "      <td>Preforation सूरज</td>\n",
              "      <td>Preforation sun</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a5c0ebb-23c8-49e5-bc1c-e71a00051990')\"\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9a5c0ebb-23c8-49e5-bc1c-e71a00051990 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-9a5c0ebb-23c8-49e5-bc1c-e71a00051990');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2a633c08-14df-4acd-a5d6-77ed422c35d7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2a633c08-14df-4acd-a5d6-77ed422c35d7')\"\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",
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              "    <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",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "        google.colab.notebook.generateWithVariable('result_df');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import pipeline\n",
        "from nltk.sentiment import SentimentIntensityAnalyzer\n",
        "# from textblob import TextBlob\n",
        "# from pattern.en import sentiment\n",
        "\n",
        "import nltk\n",
        "nltk.download('vader_lexicon')\n",
        "\n",
        "# Load BERT sentiment analysis pipeline\n",
        "bert_sentiment_pipeline = pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')\n",
        "\n",
        "# Initialize VADER SentimentIntensityAnalyzer\n",
        "vader_analyzer = SentimentIntensityAnalyzer()\n",
        "\n",
        "# Function to apply BERT sentiment analysis\n",
        "def bert_sentiment_analysis(text):\n",
        "    result = bert_sentiment_pipeline(text)[0]\n",
        "    return convert_to_label(result)\n",
        "\n",
        "# Function to apply VADER sentiment analysis\n",
        "def vader_sentiment_analysis(text):\n",
        "    sentiment_score = vader_analyzer.polarity_scores(text)\n",
        "    if sentiment_score['compound'] >= 0.05:\n",
        "        return 'Positive'\n",
        "    elif sentiment_score['compound'] <= -0.05:\n",
        "        return 'Negative'\n",
        "    else:\n",
        "        return 'Neutral'\n",
        "\n",
        "# Function to apply TextBlob sentiment analysis\n",
        "# def textblob_sentiment_analysis(text):\n",
        "#     analysis = TextBlob(text)\n",
        "#     if analysis.sentiment.polarity > 0:\n",
        "#         return 'Positive'\n",
        "#     elif analysis.sentiment.polarity < 0:\n",
        "#         return 'Negative'\n",
        "#     else:\n",
        "#         return 'Neutral'\n",
        "\n",
        "# # Function to apply Pattern sentiment analysis\n",
        "# def pattern_sentiment_analysis(text):\n",
        "#     sentiment_score = sentiment(text)\n",
        "#     if sentiment_score[0] > 0:\n",
        "#         return 'Positive'\n",
        "#     elif sentiment_score[0] < 0:\n",
        "#         return 'Negative'\n",
        "#     else:\n",
        "#         return 'Neutral'\n",
        "\n",
        "# Function to convert BERT sentiment label to human-readable label\n",
        "def convert_to_label(sentiment_score):\n",
        "    if sentiment_score['label'] == '1 star' or sentiment_score['label'] == '2 stars':\n",
        "        return 'Negative'\n",
        "    elif sentiment_score['label'] == '4 stars' or sentiment_score['label'] == '5 stars':\n",
        "        return 'Positive'\n",
        "    else:\n",
        "        return 'Neutral'\n",
        "\n",
        "# Function to create an ensemble sentiment score\n",
        "def ensemble_sentiment(row):\n",
        "    votes = [row['bert_sentiment'], row['vader_sentiment'],row['lstm_sentiment']]\n",
        "\n",
        "    # Count the votes\n",
        "    counts = {vote: votes.count(vote) for vote in set(votes)}\n",
        "\n",
        "    # Choose the sentiment with the most votes\n",
        "    max_votes = max(counts.values())\n",
        "    winners = [sentiment for sentiment, count in counts.items() if count == max_votes]\n",
        "\n",
        "    # If there's a tie, choose the sentiment with the highest priority (e.g., favoring BERT)\n",
        "    if len(winners) > 1:\n",
        "        return 'Neutral'  # You can customize the tiebreaker strategy\n",
        "\n",
        "    return winners[0]\n",
        "\n",
        "from keras.models import load_model\n",
        "import pickle\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "# Load the saved model\n",
        "loaded_model = load_model(\"/content/drive/MyDrive/yt/sentiment_model.h5\")\n",
        "\n",
        "# Load the tokenizer\n",
        "with open(\"/content/drive/MyDrive/yt/tokenizer.pkl\", \"rb\") as tokenizer_file:\n",
        "    loaded_tokenizer = pickle.load(tokenizer_file)\n",
        "\n",
        "def predict_lstm_sentiment(model, tokenizer, text):\n",
        "    # Tokenize and pad the input text\n",
        "    sequences = tokenizer.texts_to_sequences([text])\n",
        "    padded_sequences = pad_sequences(sequences, maxlen=model.input_shape[1])\n",
        "\n",
        "    # Make predictions\n",
        "    probability = model.predict(padded_sequences)[0]\n",
        "\n",
        "    # Convert probability to sentiment label\n",
        "    sentiment_label = \"Positive\" if probability > 0.5 else \"Negative\"\n",
        "\n",
        "    # Convert sentiment label to target value\n",
        "    target_value = 1 if sentiment_label == \"Positive\" else -1\n",
        "\n",
        "    return sentiment_label\n",
        "\n",
        "# Function to apply LSTM sentiment analysis\n",
        "def lstm_sentiment_analysis(text):\n",
        "    return predict_lstm_sentiment(loaded_model, loaded_tokenizer, text)"
      ],
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      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "sortt=dict()\n",
        "import pickle\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "for i in video_names:\n",
        "    df=video_dataframes[i]\n",
        "\n",
        "    df['up_comm'] = df['Comments'].apply(preprocess_text)\n",
        "\n",
        "    # Apply the logic to the \"up_comm\" column\n",
        "    data = []\n",
        "    for sentence in df['up_comm']:\n",
        "        words = process_sentence(sentence)\n",
        "        detected_languages = detect_language(sentence)\n",
        "        word_language_dict = detect_languages_for_words(words)\n",
        "        sent_type = determine_sentence_type(detected_languages)\n",
        "\n",
        "        if sent_type == \"Unknown\":\n",
        "            detected_lang_googletrans = detect_language_googletrans(sentence)\n",
        "\n",
        "\n",
        "            # Check if detected_lang_googletrans is None\n",
        "            if detected_lang_googletrans is not None:\n",
        "                words_transliterated = transliterate_words(words, detected_lang_googletrans.upper())\n",
        "                sentence_data = {\n",
        "                'Sentence': sentence,\n",
        "                'Words': words,\n",
        "                'Detected Languages': detected_languages,\n",
        "                'Type': sent_type,\n",
        "                'Detected Language (Each Word)': word_language_dict,\n",
        "                'Detected Language (Googletrans)': detected_lang_googletrans,\n",
        "                'Transliterated Words': ' '.join(words_transliterated),\n",
        "                'Final Sentence': ' '.join(words_transliterated),  # Use Transliterated Words for Unknown type\n",
        "                'FINALLL': translate_to_english1(' '.join(words_transliterated))\n",
        "                }\n",
        "            else:\n",
        "                # Handle the case where language detection failed\n",
        "                sentence_data = {\n",
        "                'Sentence': sentence,\n",
        "                'Words': words,\n",
        "                'Detected Languages': detected_languages,\n",
        "                'Type': sent_type,\n",
        "                'Detected Language (Each Word)': word_language_dict,\n",
        "                'Detected Language (Googletrans)': detected_lang_googletrans,\n",
        "                'Final Sentence': sentence,  # Use Transliterated Words for Unknown type\n",
        "                'FINALLL':sentence\n",
        "                }\n",
        "\n",
        "\n",
        "        elif sent_type == \"mixed\":\n",
        "          value_to_delete = \"Unknown\"\n",
        "\n",
        "          delete_pair_by_value(word_language_dict, value_to_delete)\n",
        "\n",
        "          # Create a sentence with space between each word\n",
        "          sentence1 = ' '.join(word_language_dict.keys())\n",
        "\n",
        "          english_translation = translate_to_english(sentence1)\n",
        "\n",
        "          sentence_data = {\n",
        "              'Sentence': sentence,\n",
        "              'Words': words,\n",
        "              'Detected Languages': detected_languages,\n",
        "              'Type': sent_type,\n",
        "              'Detected Language (Each Word)': word_language_dict,\n",
        "              'Translated to English': english_translation,\n",
        "              'Final Sentence': english_translation,  # Use Translated to English for Mixed type\n",
        "              'FINALLL':  translate_to_english1(english_translation)\n",
        "          }\n",
        "        else:\n",
        "            sentence_data = {\n",
        "                'Sentence': sentence,\n",
        "                'Words': words,\n",
        "                'Detected Languages': detected_languages,\n",
        "                'Type': sent_type,\n",
        "                'Detected Language (Each Word)': word_language_dict,\n",
        "                'Final Sentence': sentence,  # Use Sentence for other types\n",
        "                'FINALLL': translate_to_english1(sentence)\n",
        "            }\n",
        "\n",
        "        data.append(sentence_data)\n",
        "\n",
        "    # Create a DataFrame\n",
        "    df = pd.DataFrame(data)\n",
        "    df = df.dropna(subset=['FINALLL'])\n",
        "\n",
        "\n",
        "    # # Reorder columns to have \"Final Sentence\" at the end\n",
        "    # column_order = ['Sentence', 'Words', 'Detected Languages', 'Type', 'Detected Language (Each Word)',\n",
        "    #                  'Detected Language (Googletrans)', 'Transliterated Words', 'Translated to English', 'Final Sentence']\n",
        "\n",
        "    # result_df = result_df[column_order]\n",
        "    # df\n",
        "\n",
        "\n",
        "    # Apply sentiment analysis to the DataFrame using inbuilt funcs\n",
        "    df['bert_sentiment'] = df['FINALLL'].apply(bert_sentiment_analysis)\n",
        "    df['vader_sentiment'] = df['FINALLL'].apply(vader_sentiment_analysis)\n",
        "    # Apply LSTM sentiment analysis to the DataFrame\n",
        "    df['lstm_sentiment'] = df['FINALLL'].apply(lstm_sentiment_analysis)\n",
        "\n",
        "    # Apply ensemble sentiment analysis to the DataFrame\n",
        "    df['final_ensemble_sentiment'] = df.apply(ensemble_sentiment, axis=1)\n",
        "\n",
        "\n",
        "    print(df.head())\n",
        "\n",
        "\n",
        "    final_sentiment = dict(df['final_ensemble_sentiment'].value_counts())\n",
        "    print(final_sentiment)\n",
        "\n",
        "    total = sum(final_sentiment.values())\n",
        "    print(total)\n",
        "\n",
        "    positive_count = final_sentiment.get(\"Positive\", 0)\n",
        "    negative_count = final_sentiment.get(\"Negative\", 0)\n",
        "\n",
        "    # Handle the case where negative_count is zero\n",
        "    if negative_count == 0:\n",
        "        score = 1.0  # If negative_count is zero, set score to 1.0 (completely positive)\n",
        "    else:\n",
        "        score = (positive_count + negative_count) / total\n",
        "\n",
        "\n",
        "    print(score)\n",
        "\n",
        "    sortt[score] = i\n",
        "\n",
        "    print(\"___________________________________________________________________________________________________\")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJy-HBbb8fCe",
        "outputId": "ab3398ea-042d-427b-bfa9-8720028acf6f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "                                            Sentence  \\\n",
            "0  thank you so much you explained it very well b...   \n",
            "1                 while ture thanks from bangladesh    \n",
            "2                           what was that extension    \n",
            "3  after writing same code it is giving error arr...   \n",
            "4                        thanks microsoft wali didi    \n",
            "\n",
            "                                               Words  \\\n",
            "0  [thank, you, so, much, you, explained, it, ver...   \n",
            "1            [while, ture, thanks, from, bangladesh]   \n",
            "2                       [what, was, that, extension]   \n",
            "3  [after, writing, same, code, it, is, giving, e...   \n",
            "4                    [thanks, microsoft, wali, didi]   \n",
            "\n",
            "         Detected Languages     Type  \\\n",
            "0  ((0, 134, ENGLISH, en),)  ENGLISH   \n",
            "1   ((0, 34, ENGLISH, en),)  ENGLISH   \n",
            "2   ((0, 24, Unknown, un),)  Unknown   \n",
            "3  ((0, 143, ENGLISH, en),)  ENGLISH   \n",
            "4   ((0, 27, Unknown, un),)  Unknown   \n",
            "\n",
            "                       Detected Language (Each Word)  \\\n",
            "0  {'thank': 'ENGLISH', 'you': 'ENGLISH', 'so': '...   \n",
            "1  {'while': 'ENGLISH', 'ture': 'Unknown', 'thank...   \n",
            "2  {'what': 'ENGLISH', 'was': 'ENGLISH', 'that': ...   \n",
            "3  {'after': 'ENGLISH', 'writing': 'ENGLISH', 'sa...   \n",
            "4  {'thanks': 'ENGLISH', 'microsoft': 'Unknown', ...   \n",
            "\n",
            "                                      Final Sentence  \\\n",
            "0  thank you so much you explained it very well b...   \n",
            "1                 while ture thanks from bangladesh    \n",
            "2                          vhat vas that ekShtension   \n",
            "3  after writing same code it is giving error arr...   \n",
            "4                        thanks michrosoft vali didi   \n",
            "\n",
            "                                             FINALLL  \\\n",
            "0  thank you so much you explained it very well b...   \n",
            "1                  while true thanks from bangladesh   \n",
            "2                            What was that extension   \n",
            "3  after writing same code it is giving error arr...   \n",
            "4                         thanks microsoft wali didi   \n",
            "\n",
            "  Detected Language (Googletrans)         Transliterated Words  \\\n",
            "0                             NaN                          NaN   \n",
            "1                             NaN                          NaN   \n",
            "2                         English    vhat vas that ekShtension   \n",
            "3                             NaN                          NaN   \n",
            "4                         English  thanks michrosoft vali didi   \n",
            "\n",
            "  Translated to English bert_sentiment vader_sentiment lstm_sentiment  \\\n",
            "0                   NaN       Positive        Positive       Positive   \n",
            "1                   NaN       Positive        Positive       Positive   \n",
            "2                   NaN       Negative         Neutral       Negative   \n",
            "3                   NaN       Negative        Positive       Negative   \n",
            "4                   NaN       Positive        Positive       Negative   \n",
            "\n",
            "  final_ensemble_sentiment  \n",
            "0                 Positive  \n",
            "1                 Positive  \n",
            "2                 Negative  \n",
            "3                 Negative  \n",
            "4                 Positive  \n",
            "{'Positive': 137, 'Neutral': 61, 'Negative': 47}\n",
            "245\n",
            "0.7510204081632653\n",
            "___________________________________________________________________________________________________\n",
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            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 124ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 151ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 127ms/step\n",
            "1/1 [==============================] - 0s 203ms/step\n",
            "1/1 [==============================] - 0s 266ms/step\n",
            "1/1 [==============================] - 0s 215ms/step\n",
            "1/1 [==============================] - 0s 231ms/step\n",
            "1/1 [==============================] - 0s 253ms/step\n",
            "1/1 [==============================] - 0s 274ms/step\n",
            "1/1 [==============================] - 0s 242ms/step\n",
            "1/1 [==============================] - 0s 243ms/step\n",
            "1/1 [==============================] - 0s 240ms/step\n",
            "1/1 [==============================] - 0s 246ms/step\n",
            "1/1 [==============================] - 0s 253ms/step\n",
            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 274ms/step\n",
            "1/1 [==============================] - 0s 236ms/step\n",
            "1/1 [==============================] - 0s 272ms/step\n",
            "1/1 [==============================] - 0s 250ms/step\n",
            "1/1 [==============================] - 0s 152ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 153ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 149ms/step\n",
            "1/1 [==============================] - 0s 127ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 159ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 126ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 123ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 149ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 126ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 252ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 250ms/step\n",
            "1/1 [==============================] - 0s 275ms/step\n",
            "1/1 [==============================] - 0s 252ms/step\n",
            "1/1 [==============================] - 0s 248ms/step\n",
            "1/1 [==============================] - 0s 247ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 243ms/step\n",
            "1/1 [==============================] - 0s 287ms/step\n",
            "1/1 [==============================] - 0s 217ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 261ms/step\n",
            "1/1 [==============================] - 0s 153ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 162ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 152ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 128ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 125ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "                                            Sentence  \\\n",
            "0    you are amazing broo just keep going i love it    \n",
            "1  my go to video when i need to write a mergesor...   \n",
            "2  should i account for the overflow that will ha...   \n",
            "3                            pure gold explainaition   \n",
            "4                        thank you so much for this    \n",
            "\n",
            "                                               Words  \\\n",
            "0  [you, are, amazing, broo, just, keep, going, i...   \n",
            "1  [my, go, to, video, when, i, need, to, write, ...   \n",
            "2  [should, i, account, for, the, overflow, that,...   \n",
            "3                        [pure, gold, explainaition]   \n",
            "4                  [thank, you, so, much, for, this]   \n",
            "\n",
            "         Detected Languages     Type  \\\n",
            "0   ((0, 47, ENGLISH, en),)  ENGLISH   \n",
            "1  ((0, 466, ENGLISH, en),)  ENGLISH   \n",
            "2  ((0, 407, ENGLISH, en),)  ENGLISH   \n",
            "3   ((0, 23, ENGLISH, en),)  ENGLISH   \n",
            "4   ((0, 27, ENGLISH, en),)  ENGLISH   \n",
            "\n",
            "                       Detected Language (Each Word)  \\\n",
            "0  {'you': 'ENGLISH', 'are': 'ENGLISH', 'amazing'...   \n",
            "1  {'my': 'ENGLISH', 'go': 'ENGLISH', 'to': 'ENGL...   \n",
            "2  {'should': 'ENGLISH', 'i': 'ENGLISH', 'account...   \n",
            "3  {'pure': 'ENGLISH', 'gold': 'ENGLISH', 'explai...   \n",
            "4  {'thank': 'ENGLISH', 'you': 'ENGLISH', 'so': '...   \n",
            "\n",
            "                                      Final Sentence  \\\n",
            "0    you are amazing broo just keep going i love it    \n",
            "1  my go to video when i need to write a mergesor...   \n",
            "2  should i account for the overflow that will ha...   \n",
            "3                            pure gold explainaition   \n",
            "4                        thank you so much for this    \n",
            "\n",
            "                                             FINALLL  \\\n",
            "0     you are amazing broo just keep going i love it   \n",
            "1  my go to video when i need to write a mergesor...   \n",
            "2  should i account for the overflow that will ha...   \n",
            "3                              pure gold explanation   \n",
            "4                         thank you so much for this   \n",
            "\n",
            "  Detected Language (Googletrans) Transliterated Words bert_sentiment  \\\n",
            "0                             NaN                  NaN       Positive   \n",
            "1                             NaN                  NaN       Positive   \n",
            "2                             NaN                  NaN       Negative   \n",
            "3                             NaN                  NaN       Positive   \n",
            "4                             NaN                  NaN       Positive   \n",
            "\n",
            "  vader_sentiment lstm_sentiment final_ensemble_sentiment  \n",
            "0        Positive       Positive                 Positive  \n",
            "1        Positive       Positive                 Positive  \n",
            "2         Neutral       Positive                  Neutral  \n",
            "3         Neutral       Negative                  Neutral  \n",
            "4        Positive       Positive                 Positive  \n",
            "{'Positive': 204, 'Negative': 21, 'Neutral': 19}\n",
            "244\n",
            "0.9221311475409836\n",
            "___________________________________________________________________________________________________\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 161ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 151ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 156ms/step\n",
            "1/1 [==============================] - 0s 126ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 159ms/step\n",
            "1/1 [==============================] - 0s 128ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 166ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 124ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 151ms/step\n",
            "1/1 [==============================] - 0s 128ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 241ms/step\n",
            "1/1 [==============================] - 0s 246ms/step\n",
            "1/1 [==============================] - 0s 271ms/step\n",
            "1/1 [==============================] - 0s 255ms/step\n",
            "1/1 [==============================] - 0s 251ms/step\n",
            "1/1 [==============================] - 0s 254ms/step\n",
            "1/1 [==============================] - 0s 245ms/step\n",
            "1/1 [==============================] - 0s 241ms/step\n",
            "1/1 [==============================] - 0s 210ms/step\n",
            "1/1 [==============================] - 0s 252ms/step\n",
            "1/1 [==============================] - 0s 245ms/step\n",
            "1/1 [==============================] - 0s 263ms/step\n",
            "1/1 [==============================] - 0s 261ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 234ms/step\n",
            "1/1 [==============================] - 0s 216ms/step\n",
            "1/1 [==============================] - 0s 158ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 161ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 152ms/step\n",
            "1/1 [==============================] - 0s 194ms/step\n",
            "1/1 [==============================] - 0s 178ms/step\n",
            "1/1 [==============================] - 0s 166ms/step\n",
            "1/1 [==============================] - 0s 180ms/step\n",
            "1/1 [==============================] - 0s 209ms/step\n",
            "1/1 [==============================] - 0s 174ms/step\n",
            "1/1 [==============================] - 0s 181ms/step\n",
            "1/1 [==============================] - 0s 208ms/step\n",
            "1/1 [==============================] - 0s 246ms/step\n",
            "1/1 [==============================] - 0s 244ms/step\n",
            "1/1 [==============================] - 0s 173ms/step\n",
            "1/1 [==============================] - 0s 178ms/step\n",
            "1/1 [==============================] - 0s 175ms/step\n",
            "1/1 [==============================] - 0s 169ms/step\n",
            "1/1 [==============================] - 0s 161ms/step\n",
            "1/1 [==============================] - 0s 170ms/step\n",
            "1/1 [==============================] - 0s 374ms/step\n",
            "1/1 [==============================] - 1s 522ms/step\n",
            "1/1 [==============================] - 0s 313ms/step\n",
            "1/1 [==============================] - 0s 371ms/step\n",
            "1/1 [==============================] - 0s 339ms/step\n",
            "1/1 [==============================] - 0s 357ms/step\n",
            "1/1 [==============================] - 0s 274ms/step\n",
            "1/1 [==============================] - 0s 223ms/step\n",
            "1/1 [==============================] - 0s 260ms/step\n",
            "1/1 [==============================] - 0s 222ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 247ms/step\n",
            "1/1 [==============================] - 0s 224ms/step\n",
            "1/1 [==============================] - 0s 227ms/step\n",
            "1/1 [==============================] - 0s 240ms/step\n",
            "1/1 [==============================] - 0s 251ms/step\n",
            "1/1 [==============================] - 0s 250ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 152ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 129ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "                                            Sentence  \\\n",
            "0  you are the best bro i never had it means to t...   \n",
            "1  after it means to too two years this is still ...   \n",
            "2            thanks for the content i have a comment   \n",
            "3        good game or got to go or giggling my negga   \n",
            "4  what would the th iteracion mean i have a ques...   \n",
            "\n",
            "                                               Words  \\\n",
            "0  [you, are, the, best, bro, i, never, had, it, ...   \n",
            "1  [after, it, means, to, too, two, years, this, ...   \n",
            "2   [thanks, for, the, content, i, have, a, comment]   \n",
            "3  [good, game, or, got, to, go, or, giggling, my...   \n",
            "4  [what, would, the, th, iteracion, mean, i, hav...   \n",
            "\n",
            "         Detected Languages     Type  \\\n",
            "0   ((0, 98, ENGLISH, en),)  ENGLISH   \n",
            "1  ((0, 177, ENGLISH, en),)  ENGLISH   \n",
            "2   ((0, 39, ENGLISH, en),)  ENGLISH   \n",
            "3   ((0, 43, ENGLISH, en),)  ENGLISH   \n",
            "4   ((0, 50, ENGLISH, en),)  ENGLISH   \n",
            "\n",
            "                       Detected Language (Each Word)  \\\n",
            "0  {'you': 'ENGLISH', 'are': 'ENGLISH', 'the': 'E...   \n",
            "1  {'after': 'ENGLISH', 'it': 'ENGLISH', 'means':...   \n",
            "2  {'thanks': 'ENGLISH', 'for': 'ENGLISH', 'the':...   \n",
            "3  {'good': 'ENGLISH', 'game': 'ENGLISH', 'or': '...   \n",
            "4  {'what': 'ENGLISH', 'would': 'ENGLISH', 'the':...   \n",
            "\n",
            "                                      Final Sentence  \\\n",
            "0  you are the best bro i never had it means to t...   \n",
            "1  after it means to too two years this is still ...   \n",
            "2            thanks for the content i have a comment   \n",
            "3        good game or got to go or giggling my negga   \n",
            "4  what would the th iteracion mean i have a ques...   \n",
            "\n",
            "                                             FINALLL  \\\n",
            "0  you are the best bro i never had it means to t...   \n",
            "1  after it means to too two years this is still ...   \n",
            "2            thanks for the content i have a comment   \n",
            "3        good game or got to go or giggling my negga   \n",
            "4  what would the th iteracion mean i have a ques...   \n",
            "\n",
            "  Detected Language (Googletrans) Transliterated Words bert_sentiment  \\\n",
            "0                             NaN                  NaN       Positive   \n",
            "1                             NaN                  NaN       Positive   \n",
            "2                             NaN                  NaN       Positive   \n",
            "3                             NaN                  NaN       Positive   \n",
            "4                             NaN                  NaN        Neutral   \n",
            "\n",
            "  vader_sentiment lstm_sentiment final_ensemble_sentiment  \n",
            "0        Positive       Positive                 Positive  \n",
            "1        Positive       Positive                 Positive  \n",
            "2        Positive       Positive                 Positive  \n",
            "3        Positive       Positive                 Positive  \n",
            "4         Neutral       Positive                  Neutral  \n",
            "{'Positive': 102, 'Negative': 15, 'Neutral': 14}\n",
            "131\n",
            "0.8931297709923665\n",
            "___________________________________________________________________________________________________\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 296ms/step\n",
            "1/1 [==============================] - 0s 246ms/step\n",
            "1/1 [==============================] - 0s 221ms/step\n",
            "1/1 [==============================] - 0s 182ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 124ms/step\n",
            "1/1 [==============================] - 0s 127ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 149ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 156ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
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            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "                                            Sentence  \\\n",
            "0                  then where is the merge function    \n",
            "1                    make more indian accent please    \n",
            "2  perfect explanation once again thank you very ...   \n",
            "3  i am learning from your videos after years vid...   \n",
            "4                         your voice sounds so fresh   \n",
            "\n",
            "                                               Words  \\\n",
            "0            [then, where, is, the, merge, function]   \n",
            "1               [make, more, indian, accent, please]   \n",
            "2  [perfect, explanation, once, again, thank, you...   \n",
            "3  [i, am, learning, from, your, videos, after, y...   \n",
            "4                   [your, voice, sounds, so, fresh]   \n",
            "\n",
            "         Detected Languages     Type  \\\n",
            "0   ((0, 33, ENGLISH, en),)  ENGLISH   \n",
            "1   ((0, 31, ENGLISH, en),)  ENGLISH   \n",
            "2   ((0, 51, ENGLISH, en),)  ENGLISH   \n",
            "3  ((0, 167, ENGLISH, en),)  ENGLISH   \n",
            "4   ((0, 26, ENGLISH, en),)  ENGLISH   \n",
            "\n",
            "                       Detected Language (Each Word)  \\\n",
            "0  {'then': 'ENGLISH', 'where': 'ENGLISH', 'is': ...   \n",
            "1  {'make': 'ENGLISH', 'more': 'ENGLISH', 'indian...   \n",
            "2  {'perfect': 'ENGLISH', 'explanation': 'ENGLISH...   \n",
            "3  {'i': 'ENGLISH', 'am': 'ENGLISH', 'learning': ...   \n",
            "4  {'your': 'ENGLISH', 'voice': 'ENGLISH', 'sound...   \n",
            "\n",
            "                                      Final Sentence  \\\n",
            "0                  then where is the merge function    \n",
            "1                    make more indian accent please    \n",
            "2  perfect explanation once again thank you very ...   \n",
            "3  i am learning from your videos after years vid...   \n",
            "4                         your voice sounds so fresh   \n",
            "\n",
            "                                             FINALLL  \\\n",
            "0                   then where is the merge function   \n",
            "1                     make more indian accent please   \n",
            "2  perfect explanation once again thank you very ...   \n",
            "3  i am learning from your videos after years vid...   \n",
            "4                         your voice sounds so fresh   \n",
            "\n",
            "  Detected Language (Googletrans) Transliterated Words Translated to English  \\\n",
            "0                             NaN                  NaN                   NaN   \n",
            "1                             NaN                  NaN                   NaN   \n",
            "2                             NaN                  NaN                   NaN   \n",
            "3                             NaN                  NaN                   NaN   \n",
            "4                             NaN                  NaN                   NaN   \n",
            "\n",
            "  bert_sentiment vader_sentiment lstm_sentiment final_ensemble_sentiment  \n",
            "0        Neutral         Neutral       Positive                  Neutral  \n",
            "1        Neutral        Positive       Negative                  Neutral  \n",
            "2       Positive        Positive       Positive                 Positive  \n",
            "3       Positive        Positive       Positive                 Positive  \n",
            "4       Positive        Positive       Negative                 Positive  \n",
            "{'Positive': 317, 'Neutral': 87, 'Negative': 62}\n",
            "466\n",
            "0.8133047210300429\n",
            "___________________________________________________________________________________________________\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 123ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 159ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 164ms/step\n",
            "1/1 [==============================] - 0s 212ms/step\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 245ms/step\n",
            "1/1 [==============================] - 0s 238ms/step\n",
            "1/1 [==============================] - 0s 268ms/step\n",
            "1/1 [==============================] - 0s 262ms/step\n",
            "1/1 [==============================] - 0s 227ms/step\n",
            "1/1 [==============================] - 0s 275ms/step\n",
            "1/1 [==============================] - 0s 274ms/step\n",
            "1/1 [==============================] - 0s 248ms/step\n",
            "1/1 [==============================] - 0s 253ms/step\n",
            "1/1 [==============================] - 0s 268ms/step\n",
            "1/1 [==============================] - 0s 220ms/step\n",
            "1/1 [==============================] - 0s 238ms/step\n",
            "1/1 [==============================] - 0s 228ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 156ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 137ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 152ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 135ms/step\n",
            "1/1 [==============================] - 0s 127ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 158ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 131ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 149ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 160ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 136ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 155ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 144ms/step\n",
            "1/1 [==============================] - 0s 156ms/step\n",
            "1/1 [==============================] - 0s 142ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 126ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 154ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 251ms/step\n",
            "1/1 [==============================] - 0s 252ms/step\n",
            "1/1 [==============================] - 0s 234ms/step\n",
            "1/1 [==============================] - 0s 247ms/step\n",
            "1/1 [==============================] - 0s 249ms/step\n",
            "1/1 [==============================] - 0s 256ms/step\n",
            "1/1 [==============================] - 0s 244ms/step\n",
            "1/1 [==============================] - 0s 280ms/step\n",
            "1/1 [==============================] - 0s 235ms/step\n",
            "1/1 [==============================] - 0s 235ms/step\n",
            "1/1 [==============================] - 0s 252ms/step\n",
            "1/1 [==============================] - 0s 251ms/step\n",
            "1/1 [==============================] - 0s 251ms/step\n",
            "1/1 [==============================] - 0s 236ms/step\n",
            "1/1 [==============================] - 0s 237ms/step\n",
            "1/1 [==============================] - 0s 230ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 146ms/step\n",
            "1/1 [==============================] - 0s 148ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 157ms/step\n",
            "1/1 [==============================] - 0s 134ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 138ms/step\n",
            "1/1 [==============================] - 0s 154ms/step\n",
            "1/1 [==============================] - 0s 130ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 129ms/step\n",
            "1/1 [==============================] - 0s 132ms/step\n",
            "1/1 [==============================] - 0s 158ms/step\n",
            "1/1 [==============================] - 0s 149ms/step\n",
            "1/1 [==============================] - 0s 140ms/step\n",
            "1/1 [==============================] - 0s 139ms/step\n",
            "1/1 [==============================] - 0s 133ms/step\n",
            "1/1 [==============================] - 0s 147ms/step\n",
            "1/1 [==============================] - 0s 145ms/step\n",
            "1/1 [==============================] - 0s 143ms/step\n",
            "1/1 [==============================] - 0s 141ms/step\n",
            "1/1 [==============================] - 0s 150ms/step\n",
            "                                            Sentence  \\\n",
            "0  best explanation than other youtubers hope you...   \n",
            "1  hey kyonal when yo say i am not going to teach...   \n",
            "2  mergesort will be called again and again but m...   \n",
            "3  how after dividing arrays in halfs those halfs...   \n",
            "4   inplace sort smjh nhi aaya merge kaise hua usme    \n",
            "\n",
            "                                               Words  \\\n",
            "0  [best, explanation, than, other, youtubers, ho...   \n",
            "1  [hey, kyonal, when, yo, say, i, am, not, going...   \n",
            "2  [mergesort, will, be, called, again, and, agai...   \n",
            "3  [how, after, dividing, arrays, in, halfs, thos...   \n",
            "4  [inplace, sort, smjh, nhi, aaya, merge, kaise,...   \n",
            "\n",
            "         Detected Languages     Type  \\\n",
            "0  ((0, 119, ENGLISH, en),)  ENGLISH   \n",
            "1  ((0, 127, ENGLISH, en),)  ENGLISH   \n",
            "2   ((0, 93, ENGLISH, en),)  ENGLISH   \n",
            "3   ((0, 66, ENGLISH, en),)  ENGLISH   \n",
            "4   ((0, 48, Unknown, un),)  Unknown   \n",
            "\n",
            "                       Detected Language (Each Word)  \\\n",
            "0  {'best': 'ENGLISH', 'explanation': 'ENGLISH', ...   \n",
            "1  {'hey': 'ENGLISH', 'kyonal': 'Unknown', 'when'...   \n",
            "2  {'mergesort': 'Unknown', 'will': 'ENGLISH', 'b...   \n",
            "3  {'how': 'ENGLISH', 'after': 'ENGLISH', 'dividi...   \n",
            "4  {'inplace': 'Unknown', 'sort': 'ENGLISH', 'smj...   \n",
            "\n",
            "                                      Final Sentence  \\\n",
            "0  best explanation than other youtubers hope you...   \n",
            "1  hey kyonal when yo say i am not going to teach...   \n",
            "2  mergesort will be called again and again but m...   \n",
            "3  how after dividing arrays in halfs those halfs...   \n",
            "4  इन्प्लचे सोर्त् स्म्झ् न्हि आय मेर्गे कैसे हुअ...   \n",
            "\n",
            "                                             FINALLL  \\\n",
            "0  best explanation than other youtubers hope you...   \n",
            "1  hey kyonal when yo say i am not going to teach...   \n",
            "2  mergesort will be called again and again but m...   \n",
            "3  how after dividing arrays in halfs those halfs...   \n",
            "4                How did I I am in the heart of Sort   \n",
            "\n",
            "  Detected Language (Googletrans)  \\\n",
            "0                             NaN   \n",
            "1                             NaN   \n",
            "2                             NaN   \n",
            "3                             NaN   \n",
            "4                           Hindi   \n",
            "\n",
            "                                Transliterated Words Translated to English  \\\n",
            "0                                                NaN                   NaN   \n",
            "1                                                NaN                   NaN   \n",
            "2                                                NaN                   NaN   \n",
            "3                                                NaN                   NaN   \n",
            "4  इन्प्लचे सोर्त् स्म्झ् न्हि आय मेर्गे कैसे हुअ...                   NaN   \n",
            "\n",
            "  bert_sentiment vader_sentiment lstm_sentiment final_ensemble_sentiment  \n",
            "0       Positive        Positive       Positive                 Positive  \n",
            "1       Positive        Positive       Positive                 Positive  \n",
            "2        Neutral         Neutral       Negative                  Neutral  \n",
            "3        Neutral         Neutral       Negative                  Neutral  \n",
            "4       Positive         Neutral       Positive                 Positive  \n",
            "{'Positive': 73, 'Neutral': 28, 'Negative': 21}\n",
            "122\n",
            "0.7704918032786885\n",
            "___________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sortt)\n",
        "\n",
        "sortt_keys= list(sortt.keys())\n",
        "sortt_keys.sort(reverse=True)\n",
        "\n",
        "sorted_vids={i: sortt[i] for i in sortt_keys}\n",
        "\n",
        "print(sorted_vids)\n",
        "\n",
        "print(\"________________________________________________________________________________\")\n",
        "print(\"________________________________________________________________________________\")\n",
        "\n",
        "order=list(sorted_vids.values())\n",
        "print(\"PRIORITY RANKING WILL BE :\")\n",
        "for i in order:\n",
        "    print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nHDNajGMDC9",
        "outputId": "36161677-5b50-4eed-9615-2b2663f9fdef"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0.7510204081632653: 'video_1', 0.9221311475409836: 'video_2', 0.8931297709923665: 'video_3', 0.8133047210300429: 'video_4', 0.7704918032786885: 'video_5'}\n",
            "{0.9221311475409836: 'video_2', 0.8931297709923665: 'video_3', 0.8133047210300429: 'video_4', 0.7704918032786885: 'video_5', 0.7510204081632653: 'video_1'}\n",
            "________________________________________________________________________________\n",
            "________________________________________________________________________________\n",
            "PRIORITY RANKING WILL BE :\n",
            "video_2\n",
            "video_3\n",
            "video_4\n",
            "video_5\n",
            "video_1\n"
          ]
        }
      ]
    }
  ]
}