Scrap the internet (articles, social media, etc) to get what they say about Omdena using NLP libraries (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Importing all the packages\n",
    "import requests\n",
    "import urllib.request\n",
    "import time\n",
    "import spacy\n",
    "from bs4 import BeautifulSoup\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import re\n",
    "import unicodedata\n",
    "from nltk.corpus import stopwords\n",
    "import tweepy\n",
    "from textblob import TextBlob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Cleaning text \n",
    "### reference (https://github.com/kaparker/gameofthrones-wordclouds/blob/master/gotwordcloud.py)\n",
    "\n",
    "def removetitle(text):\n",
    "    return re.sub(r'.*:', '', text)\n",
    "\n",
    "def removebrackets(text):\n",
    "    return re.sub('[\\(\\[].*?[\\)\\]]', ' ', text)\n",
    "\n",
    "def remove_accented_chars(text):\n",
    "    return unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
    "\n",
    "def remove_special_chars(text, remove_digits=False):\n",
    "    pattern = r'[^a-zA-Z0-9\\s]' if not remove_digits else r'[^a-zA-Z\\s]'\n",
    "    return re.sub(pattern, '', text)\n",
    "\n",
    "def remove_stopwords(text):\n",
    "    stopword_list = stopwords.words('english')\n",
    "    tokens = nltk.word_tokenize(text)\n",
    "    tokens = [token.strip() for token in tokens]\n",
    "    return ' '.join([token for token in tokens if token not in stopword_list])\n",
    "\n",
    "def lemmatize(text):\n",
    "    text = nlp(text)\n",
    "    return ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting data from news article and other google url sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Making our google query ready\n",
    "topic=\"Omdena AI\"\n",
    "numResults=3000\n",
    "url =\"https://www.google.com/search?q=\"+topic+\"&tbm=nws&hl=en&num=\"+str(numResults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)\n",
    "soup = BeautifulSoup(response.content, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = soup.find_all(\"div\", attrs = {\"class\": \"ZINbbc\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptions = []\n",
    "for result in results:\n",
    "    try:\n",
    "        description = result.find(\"div\", attrs={\"class\":\"s3v9rd\"}).get_text()\n",
    "        if description != \"\": \n",
    "            descriptions.append(description)\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"\".join(descriptions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text.split(\".\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2 months ago · Omdena is a company that uses its expansive community of voluntary engineers to create lasting change through projects that take just two',\n",
       " '',\n",
       " '',\n",
       " '4 months ago · Omdena is a global platform that uses a bottom-up collaborative model to build AI and ML solutions',\n",
       " ' People from all over the world come',\n",
       " '',\n",
       " '',\n",
       " '12 months ago · Omdena and its Covid19 project are displayed on the Nasdaq billboard in ',\n",
       " '',\n",
       " '',\n",
       " ' Omdena is an innovation platform for building AI solutions to',\n",
       " '',\n",
       " '',\n",
       " '4 months ago · A leader in collaborative AI development, Omdena believes that truly great startups should build solutions that positively change the world by',\n",
       " '',\n",
       " '',\n",
       " '17 months ago · Omdena is a collaborative platform for building AI solutions to real-world problems through the power of bottom up collaboration',\n",
       " ' More From',\n",
       " '',\n",
       " '',\n",
       " '13 months ago · Rudradeb Mitra, is the Founder of Omdena a global platform where a community of changemakers building AI solutions to real-world problems',\n",
       " '',\n",
       " '',\n",
       " '7 months ago · Bambini introduced on Omdena, a global platform where AI experts and data scientists from diverse backgrounds collaborate to create',\n",
       " '',\n",
       " '',\n",
       " '3 weeks ago · ',\n",
       " '',\n",
       " '',\n",
       " ' the African Centre for Economic Transformation (ACET) and Omdena, ',\n",
       " '',\n",
       " '',\n",
       " \" ACET and Omdena completed Africa's first ever Artificial Intelligence\",\n",
       " '',\n",
       " '',\n",
       " '12 months ago · –based Omdena, will scour publicly available data sources and apply data visualization and AI tools to investigate how government policies are',\n",
       " '',\n",
       " '',\n",
       " '11 months ago · Omdena, a platform for building AI solutions through global collaboration, is using Labelbox to annotate the positive or negative effects of',\n",
       " '',\n",
       " '',\n",
       " '6 months ago · Safecity and Omdena',\n",
       " ' Formed after the 2012 Nirbhaya Gang-rape in India, this AI model is a collaborative technique by Omdena and Safecity',\n",
       " '',\n",
       " '',\n",
       " '2 months ago · RE-WORK is a leader in AI and Deep Learning events, organizing ',\n",
       " '',\n",
       " '',\n",
       " ' Lead Machine Learning Engineer from Omdena; Harini Suresh, PhD',\n",
       " '',\n",
       " '',\n",
       " '5 months ago · Many AI-based companies are adapting to these annotations for ',\n",
       " '',\n",
       " '',\n",
       " ' Omdena is used for labelling tasks in deep learning for tree identification',\n",
       " '4 months ago · Omdena brings together larger groups of AI professionals to solve social problems e',\n",
       " 'g',\n",
       " ', reducing rural family violence during a famine',\n",
       " '2 months ago · We teamed up with a company called Omdena that organizes AI for good challenges',\n",
       " ' We recruited over 50 data scientists in order to work with',\n",
       " '',\n",
       " '',\n",
       " '2 months ago · ',\n",
       " '',\n",
       " '',\n",
       " ' diplomacy to pharma clinical trials management to satellite telemetry and AI',\n",
       " ' ',\n",
       " '',\n",
       " '',\n",
       " ' Omdena, for example, actively engages platform members in',\n",
       " '',\n",
       " '',\n",
       " '4 months ago · Fhinck is a software installed on computers that uses artificial intelligence to ',\n",
       " '',\n",
       " '',\n",
       " ' Omdena is a global platform that uses a bottom-up collaborative model to build AI',\n",
       " '',\n",
       " '',\n",
       " '7 months ago · Omdena in India is extending freelancing into AI enabled FAST teaming in the non-profit world, and Spacely here in the US is bringing',\n",
       " '',\n",
       " '',\n",
       " \"1 month ago · Maybe it's a compelling cause which helps Omdena - collaboratively solving social problems in developing countries using AI – attract\",\n",
       " '',\n",
       " '',\n",
       " '13 months ago · One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units',\n",
       " '',\n",
       " '',\n",
       " '5 months ago · SANSA has also executed a project that uses Satellite Imagery and AI to locate informal settlements',\n",
       " ' Kenya witnessed an array of partnerships',\n",
       " '',\n",
       " '',\n",
       " '22 months ago · What is the promise of AI to radiologists? How will the future customize preventive medicine using cross-functional data, like genomics and',\n",
       " '',\n",
       " '',\n",
       " '8 months ago · In high demand tech areas like AI and machine learning, new digital marketplaces Experfy, Omdena, and Contra, and established platforms',\n",
       " '',\n",
       " '',\n",
       " '13 months ago · In addition to practicing medicine, he also enjoys programming',\n",
       " ' His recent research project AIBx uses AI to classify thyroid nodules',\n",
       " ' This is also',\n",
       " '',\n",
       " '',\n",
       " '4 months ago · Rudradeb Mitra: Rudradeb, Founder of Omdena, oversees all ',\n",
       " '',\n",
       " '',\n",
       " ' It monitors developments, recognition, and achievements made by AI, big data',\n",
       " '',\n",
       " '',\n",
       " '24 months ago · One of the upcoming startups, Omdena, is building an educational platform for AI enthusiasts and students to collaborate with organizations on',\n",
       " '',\n",
       " '',\n",
       " '13 months ago · One of their partners, Omdena, is calling on AI entrepreneurs to help governments design data-driven policy via its coronavirus challenge',\n",
       " \"12 months ago · An additional startup with a presence in Mexico is Orion Startups-backed Omdena, which recently partnered with the United Nations' AI for\",\n",
       " '',\n",
       " '',\n",
       " '12 months ago · Applying AI to Analyze Domestic Violence in Lockdowns — From No Data to Building an ML Classifier',\n",
       " ' Harshita Chopra in Omdena',\n",
       " '',\n",
       " '',\n",
       " \"14 months ago · En Turquie, des experts en IA de la plateforme collaborative Omdena AI ont développé un prototype d'IA spécialement pensé pour résoudre ce\",\n",
       " '',\n",
       " '',\n",
       " '14 months ago · ',\n",
       " '',\n",
       " '',\n",
       " ' sztucznej inteligencji i uczenia maszynowego zebranych przez Omdena w ramach \"AI for good challenge\" po to, by przebadać młodych ludzi',\n",
       " '',\n",
       " '',\n",
       " '13 months ago · Hiervoor gaat Fruitpunch AI samenwerken met Omdena, een wereldwijd AI-platform uit de VS',\n",
       " ' Ondertussen zitten bijna alle scholieren en',\n",
       " '',\n",
       " '',\n",
       " '54 months ago · 21',\n",
       " '04',\n",
       " '2020 – The AI Journal hat 30 Frauen gefragt, welche Tipps sie anderen Frauen ',\n",
       " '',\n",
       " '',\n",
       " ' Marisa Tschopp hat mit der Lern-Plattform Omdena diese und weitere',\n",
       " '',\n",
       " '',\n",
       " 'In order to show you the most relevant results, we have omitted some entries very similar to the 33 already displayed',\n",
       " '\\nIf you like, you can repeat the search with the omitted results included',\n",
       " '']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.split(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "google_text_data=[x if len(x)>20 else np.nan for x in text.split(\".\")]\n",
    "### we dont require text less than 20 characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_df=pd.DataFrame()\n",
    "g_df['Text']=google_text_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2 months ago · Omdena is a company that uses i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4 months ago · Omdena is a global platform tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>People from all over the world come</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12 months ago · Omdena and its Covid19 project...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Omdena is an innovation platform for building...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4 months ago · A leader in collaborative AI de...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17 months ago · Omdena is a collaborative plat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13 months ago · Rudradeb Mitra, is the Founder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7 months ago · Bambini introduced on Omdena, a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>the African Centre for Economic Transformatio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ACET and Omdena completed Africa's first ever...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>12 months ago · –based Omdena, will scour publ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>11 months ago · Omdena, a platform for buildin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6 months ago · Safecity and Omdena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Formed after the 2012 Nirbhaya Gang-rape in I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2 months ago · RE-WORK is a leader in AI and D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lead Machine Learning Engineer from Omdena; H...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>5 months ago · Many AI-based companies are ada...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Omdena is used for labelling tasks in deep le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4 months ago · Omdena brings together larger g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>, reducing rural family violence during a famine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2 months ago · We teamed up with a company cal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>We recruited over 50 data scientists in order...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>diplomacy to pharma clinical trials managemen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Omdena, for example, actively engages platfor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>4 months ago · Fhinck is a software installed ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Omdena is a global platform that uses a botto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>7 months ago · Omdena in India is extending fr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1 month ago · Maybe it's a compelling cause wh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>13 months ago · One of the biggest challenges ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>5 months ago · SANSA has also executed a proje...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Kenya witnessed an array of partnerships</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>22 months ago · What is the promise of AI to r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>8 months ago · In high demand tech areas like ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>13 months ago · In addition to practicing medi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>His recent research project AIBx uses AI to c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>4 months ago · Rudradeb Mitra: Rudradeb, Found...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>It monitors developments, recognition, and ac...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>24 months ago · One of the upcoming startups, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>13 months ago · One of their partners, Omdena,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>12 months ago · An additional startup with a p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>12 months ago · Applying AI to Analyze Domesti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Harshita Chopra in Omdena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>14 months ago · En Turquie, des experts en IA ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>sztucznej inteligencji i uczenia maszynowego ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>13 months ago · Hiervoor gaat Fruitpunch AI sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Ondertussen zitten bijna alle scholieren en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2020 – The AI Journal hat 30 Frauen gefragt, w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Marisa Tschopp hat mit der Lern-Plattform Omd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>In order to show you the most relevant results...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>\\nIf you like, you can repeat the search with ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Text\n",
       "0    2 months ago · Omdena is a company that uses i...\n",
       "3    4 months ago · Omdena is a global platform tha...\n",
       "4                  People from all over the world come\n",
       "7    12 months ago · Omdena and its Covid19 project...\n",
       "10    Omdena is an innovation platform for building...\n",
       "13   4 months ago · A leader in collaborative AI de...\n",
       "16   17 months ago · Omdena is a collaborative plat...\n",
       "20   13 months ago · Rudradeb Mitra, is the Founder...\n",
       "23   7 months ago · Bambini introduced on Omdena, a...\n",
       "29    the African Centre for Economic Transformatio...\n",
       "32    ACET and Omdena completed Africa's first ever...\n",
       "35   12 months ago · –based Omdena, will scour publ...\n",
       "38   11 months ago · Omdena, a platform for buildin...\n",
       "41                  6 months ago · Safecity and Omdena\n",
       "42    Formed after the 2012 Nirbhaya Gang-rape in I...\n",
       "45   2 months ago · RE-WORK is a leader in AI and D...\n",
       "48    Lead Machine Learning Engineer from Omdena; H...\n",
       "51   5 months ago · Many AI-based companies are ada...\n",
       "54    Omdena is used for labelling tasks in deep le...\n",
       "55   4 months ago · Omdena brings together larger g...\n",
       "57    , reducing rural family violence during a famine\n",
       "58   2 months ago · We teamed up with a company cal...\n",
       "59    We recruited over 50 data scientists in order...\n",
       "65    diplomacy to pharma clinical trials managemen...\n",
       "69    Omdena, for example, actively engages platfor...\n",
       "72   4 months ago · Fhinck is a software installed ...\n",
       "75    Omdena is a global platform that uses a botto...\n",
       "78   7 months ago · Omdena in India is extending fr...\n",
       "81   1 month ago · Maybe it's a compelling cause wh...\n",
       "84   13 months ago · One of the biggest challenges ...\n",
       "87   5 months ago · SANSA has also executed a proje...\n",
       "88            Kenya witnessed an array of partnerships\n",
       "91   22 months ago · What is the promise of AI to r...\n",
       "94   8 months ago · In high demand tech areas like ...\n",
       "97   13 months ago · In addition to practicing medi...\n",
       "98    His recent research project AIBx uses AI to c...\n",
       "102  4 months ago · Rudradeb Mitra: Rudradeb, Found...\n",
       "105   It monitors developments, recognition, and ac...\n",
       "108  24 months ago · One of the upcoming startups, ...\n",
       "111  13 months ago · One of their partners, Omdena,...\n",
       "112  12 months ago · An additional startup with a p...\n",
       "115  12 months ago · Applying AI to Analyze Domesti...\n",
       "116                          Harshita Chopra in Omdena\n",
       "119  14 months ago · En Turquie, des experts en IA ...\n",
       "125   sztucznej inteligencji i uczenia maszynowego ...\n",
       "128  13 months ago · Hiervoor gaat Fruitpunch AI sa...\n",
       "129        Ondertussen zitten bijna alle scholieren en\n",
       "134  2020 – The AI Journal hat 30 Frauen gefragt, w...\n",
       "137   Marisa Tschopp hat mit der Lern-Plattform Omd...\n",
       "140  In order to show you the most relevant results...\n",
       "141  \\nIf you like, you can repeat the search with ..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 months ago · Omdena is a company that uses its expansive community of voluntary engineers to create lasting change through projects that take just two\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "4 months ago · Omdena is a global platform that uses a bottom-up collaborative model to build AI and ML solutions\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " People from all over the world come\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "12 months ago · Omdena and its Covid19 project are displayed on the Nasdaq billboard in \n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Omdena is an innovation platform for building AI solutions to\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "4 months ago · A leader in collaborative AI development, Omdena believes that truly great startups should build solutions that positively change the world by\n",
      "Sentiment(polarity=0.5136363636363637, subjectivity=0.6477272727272727)\n",
      "17 months ago · Omdena is a collaborative platform for building AI solutions to real-world problems through the power of bottom up collaboration\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "13 months ago · Rudradeb Mitra, is the Founder of Omdena a global platform where a community of changemakers building AI solutions to real-world problems\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "7 months ago · Bambini introduced on Omdena, a global platform where AI experts and data scientists from diverse backgrounds collaborate to create\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " the African Centre for Economic Transformation (ACET) and Omdena, \n",
      "Sentiment(polarity=0.1, subjectivity=0.1)\n",
      " ACET and Omdena completed Africa's first ever Artificial Intelligence\n",
      "Sentiment(polarity=-0.175, subjectivity=0.6666666666666666)\n",
      "12 months ago · –based Omdena, will scour publicly available data sources and apply data visualization and AI tools to investigate how government policies are\n",
      "Sentiment(polarity=0.4, subjectivity=0.4)\n",
      "11 months ago · Omdena, a platform for building AI solutions through global collaboration, is using Labelbox to annotate the positive or negative effects of\n",
      "Sentiment(polarity=-0.024242424242424242, subjectivity=0.3151515151515151)\n",
      "6 months ago · Safecity and Omdena\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Formed after the 2012 Nirbhaya Gang-rape in India, this AI model is a collaborative technique by Omdena and Safecity\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "2 months ago · RE-WORK is a leader in AI and Deep Learning events, organizing \n",
      "Sentiment(polarity=0.0, subjectivity=0.4)\n",
      " Lead Machine Learning Engineer from Omdena; Harini Suresh, PhD\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "5 months ago · Many AI-based companies are adapting to these annotations for \n",
      "Sentiment(polarity=0.5, subjectivity=0.5)\n",
      " Omdena is used for labelling tasks in deep learning for tree identification\n",
      "Sentiment(polarity=0.0, subjectivity=0.4)\n",
      "4 months ago · Omdena brings together larger groups of AI professionals to solve social problems e\n",
      "Sentiment(polarity=0.016666666666666666, subjectivity=0.2833333333333333)\n",
      ", reducing rural family violence during a famine\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "2 months ago · We teamed up with a company called Omdena that organizes AI for good challenges\n",
      "Sentiment(polarity=0.7, subjectivity=0.6000000000000001)\n",
      " We recruited over 50 data scientists in order to work with\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " diplomacy to pharma clinical trials management to satellite telemetry and AI\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Omdena, for example, actively engages platform members in\n",
      "Sentiment(polarity=-0.13333333333333333, subjectivity=0.6)\n",
      "4 months ago · Fhinck is a software installed on computers that uses artificial intelligence to \n",
      "Sentiment(polarity=-0.6, subjectivity=1.0)\n",
      " Omdena is a global platform that uses a bottom-up collaborative model to build AI\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "7 months ago · Omdena in India is extending freelancing into AI enabled FAST teaming in the non-profit world, and Spacely here in the US is bringing\n",
      "Sentiment(polarity=0.2, subjectivity=0.6)\n",
      "1 month ago · Maybe it's a compelling cause which helps Omdena - collaboratively solving social problems in developing countries using AI – attract\n",
      "Sentiment(polarity=0.16666666666666666, subjectivity=0.3333333333333333)\n",
      "13 months ago · One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units\n",
      "Sentiment(polarity=-0.3, subjectivity=0.75)\n",
      "5 months ago · SANSA has also executed a project that uses Satellite Imagery and AI to locate informal settlements\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Kenya witnessed an array of partnerships\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "22 months ago · What is the promise of AI to radiologists? How will the future customize preventive medicine using cross-functional data, like genomics and\n",
      "Sentiment(polarity=0.0, subjectivity=0.125)\n",
      "8 months ago · In high demand tech areas like AI and machine learning, new digital marketplaces Experfy, Omdena, and Contra, and established platforms\n",
      "Sentiment(polarity=0.0987878787878788, subjectivity=0.3315151515151515)\n",
      "13 months ago · In addition to practicing medicine, he also enjoys programming\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " His recent research project AIBx uses AI to classify thyroid nodules\n",
      "Sentiment(polarity=0.0, subjectivity=0.25)\n",
      "4 months ago · Rudradeb Mitra: Rudradeb, Founder of Omdena, oversees all \n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " It monitors developments, recognition, and achievements made by AI, big data\n",
      "Sentiment(polarity=0.0, subjectivity=0.1)\n",
      "24 months ago · One of the upcoming startups, Omdena, is building an educational platform for AI enthusiasts and students to collaborate with organizations on\n",
      "Sentiment(polarity=0.25, subjectivity=0.25)\n",
      "13 months ago · One of their partners, Omdena, is calling on AI entrepreneurs to help governments design data-driven policy via its coronavirus challenge\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "12 months ago · An additional startup with a presence in Mexico is Orion Startups-backed Omdena, which recently partnered with the United Nations' AI for\n",
      "Sentiment(polarity=0.0, subjectivity=0.25)\n",
      "12 months ago · Applying AI to Analyze Domestic Violence in Lockdowns — From No Data to Building an ML Classifier\n",
      "Sentiment(polarity=0.0, subjectivity=0.1)\n",
      " Harshita Chopra in Omdena\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "14 months ago · En Turquie, des experts en IA de la plateforme collaborative Omdena AI ont développé un prototype d'IA spécialement pensé pour résoudre ce\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " sztucznej inteligencji i uczenia maszynowego zebranych przez Omdena w ramach \"AI for good challenge\" po to, by przebadać młodych ludzi\n",
      "Sentiment(polarity=0.7, subjectivity=0.6000000000000001)\n",
      "13 months ago · Hiervoor gaat Fruitpunch AI samenwerken met Omdena, een wereldwijd AI-platform uit de VS\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Ondertussen zitten bijna alle scholieren en\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "2020 – The AI Journal hat 30 Frauen gefragt, welche Tipps sie anderen Frauen \n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      " Marisa Tschopp hat mit der Lern-Plattform Omdena diese und weitere\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "In order to show you the most relevant results, we have omitted some entries very similar to the 33 already displayed\n",
      "Sentiment(polarity=0.3, subjectivity=0.64)\n",
      "\n",
      "If you like, you can repeat the search with the omitted results included\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n"
     ]
    }
   ],
   "source": [
    "for tweet in g_df['Text']:\n",
    "    print(tweet)\n",
    "    analysis = TextBlob(tweet)\n",
    "    print(analysis.sentiment)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_title=removetitle(text)\n",
    "text_brackets=removebrackets(text_title)\n",
    "text_clean=remove_accented_chars(text_brackets)\n",
    "text_clean=text_clean.lower()\n",
    "text_clean=remove_special_chars(text_clean)\n",
    "text_clean=remove_stopwords(text_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rudradeb founder omdena oversees monitors developments recognition achievements made ai big data24 months ago one upcoming startups omdena building educational platform ai enthusiasts students collaborate organizations on13 months ago one partners omdena calling ai entrepreneurs help governments design datadriven policy via coronavirus challenge12 months ago additional startup presence mexico orion startupsbacked omdena recently partnered united nations ai for12 months ago applying ai analyze domestic violence lockdowns data building ml classifier harshita chopra omdena14 months ago en turquie des experts en ia de la plateforme collaborative omdena ai ont developpe un prototype dia specialement pense pour resoudre ce14 months ago sztucznej inteligencji uczenia maszynowego zebranych przez omdena w ramach ai good challenge po przebadac modych ludzi13 months ago hiervoor gaat fruitpunch ai samenwerken met omdena een wereldwijd aiplatform uit de vs ondertussen zitten bijna alle scholieren en54 months ago 21042020 ai journal hat 30 frauen gefragt welche tipps sie anderen frauen marisa tschopp hat mit der lernplattform omdena diese und weiterein order show relevant results omitted entries similar 33 already displayed like repeat search omitted results included'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Our resultant text\n",
    "text_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us try to get data from twitter and also perform sentiment ananlysis on it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "\n",
      "RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "\n",
      "RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "\n",
      "Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, us… https://t.co/iU5RhzIQMF\n",
      "Sentiment(polarity=0.0, subjectivity=0.0)\n",
      "\n",
      "RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\n",
      "\n",
      "11 proven Do's and Don'ts you need to know to build yo…\n",
      "Sentiment(polarity=0.5, subjectivity=0.5)\n",
      "\n",
      "RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\n",
      "\n",
      "11 proven Do's and Don'ts you need to know to build yo…\n",
      "Sentiment(polarity=0.5, subjectivity=0.5)\n",
      "\n",
      "RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\n",
      "\n",
      "11 proven Do's and Don'ts you need to know to build yo…\n",
      "Sentiment(polarity=0.5, subjectivity=0.5)\n",
      "\n",
      "How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\n",
      "\n",
      "11 proven Do's and Don'ts you need to know t… https://t.co/iXzEeyjWmP\n",
      "Sentiment(polarity=0.5, subjectivity=0.5)\n",
      "\n",
      "RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\n",
      "Sentiment(polarity=0.5, subjectivity=0.8333333333333334)\n",
      "\n",
      "RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\n",
      "Sentiment(polarity=0.5, subjectivity=0.8333333333333334)\n",
      "\n",
      "RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\n",
      "Sentiment(polarity=0.5, subjectivity=0.8333333333333334)\n",
      "\n",
      "RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\n",
      "Sentiment(polarity=0.5, subjectivity=0.8333333333333334)\n",
      "\n",
      "RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\n",
      "Sentiment(polarity=0.5, subjectivity=0.8333333333333334)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Step 1 - Authenticate\n",
    "consumer_key= ''\n",
    "consumer_secret= ''\n",
    "\n",
    "access_token=''\n",
    "access_token_secret=''\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "api = tweepy.API(auth)\n",
    "\n",
    "#Step 3 - Retrieve Tweets\n",
    "public_tweets = api.search('Omdena')\n",
    "tweets_list=[]\n",
    "for tweet in public_tweets:\n",
    "    print(tweet.text)\n",
    "    tweets_list.append(tweet.text)\n",
    "    #Step 4 Perform Sentiment Analysis on Tweets\n",
    "    analysis = TextBlob(tweet.text)\n",
    "    print(analysis.sentiment)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_text=''\n",
    "for x in public_tweets:\n",
    "    twitter_text+=x.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, using tools inc…Omdena AI ( @OmdenaAI) launches a project to boost Kenya's efforts to restore degraded land in an equitable way, us… https://t.co/iU5RhzIQMFRT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\\n\\n11 proven Do's and Don'ts you need to know to build yo…RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\\n\\n11 proven Do's and Don'ts you need to know to build yo…RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\\n\\n11 proven Do's and Don'ts you need to know to build yo…How to Build a Data Science Portfolio in 2021 That Gets You a Top Job\\n\\n11 proven Do's and Don'ts you need to know t… https://t.co/iXzEeyjWmPRT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_text(txt):\n",
    "    text_title=removetitle(txt)\n",
    "    text_brackets=removebrackets(text_title)\n",
    "    text_clean=remove_accented_chars(text_brackets)\n",
    "    text_clean=text_clean.lower()\n",
    "    text_clean=remove_special_chars(text_clean)\n",
    "    text_clean=remove_stopwords(text_clean)\n",
    "    return text_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "twitter_text_clean=clean_text(twitter_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'build data science portfolio 2021 gets top job build data science portfolio 2021 gets top job build data science portfolio 2021 gets top job 11 proven dos donts need know build yohow build data science portfolio 2021 gets top job ai help prevent sexual harassment bigdata analytics datascience ai iot iiot pytorch python rstats tensor'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twitter_text_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=text_clean+twitter_text_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'rudradeb founder omdena oversees monitors developments recognition achievements made ai big data24 months ago one upcoming startups omdena building educational platform ai enthusiasts students collaborate organizations on13 months ago one partners omdena calling ai entrepreneurs help governments design datadriven policy via coronavirus challenge12 months ago additional startup presence mexico orion startupsbacked omdena recently partnered united nations ai for12 months ago applying ai analyze domestic violence lockdowns data building ml classifier harshita chopra omdena14 months ago en turquie des experts en ia de la plateforme collaborative omdena ai ont developpe un prototype dia specialement pense pour resoudre ce14 months ago sztucznej inteligencji uczenia maszynowego zebranych przez omdena w ramach ai good challenge po przebadac modych ludzi13 months ago hiervoor gaat fruitpunch ai samenwerken met omdena een wereldwijd aiplatform uit de vs ondertussen zitten bijna alle scholieren en54 months ago 21042020 ai journal hat 30 frauen gefragt welche tipps sie anderen frauen marisa tschopp hat mit der lernplattform omdena diese und weiterein order show relevant results omitted entries similar 33 already displayed like repeat search omitted results includedbuild data science portfolio 2021 gets top job build data science portfolio 2021 gets top job build data science portfolio 2021 gets top job 11 proven dos donts need know build yohow build data science portfolio 2021 gets top job ai help prevent sexual harassment bigdata analytics datascience ai iot iiot pytorch python rstats tensor'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us build some visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(stopwords=STOPWORDS).generate(final)\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### By adding some more stops words in list \n",
    "wordcloud = WordCloud(stopwords=set(list(STOPWORDS)+['day','ai','ago','hour','months','omdena'])).generate(final)\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference https://www.analyticsvidhya.com/blog/2018/11/introduction-text-summarization-textrank-python/\n",
    "\n",
    "# Summarization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference\n",
    "https://github.com/dipanjanS/text-analytics-with-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\rinki\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n",
    "nltk.download('punkt') # one time execution\n",
    "import re\n",
    "from nltk.tokenize import sent_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_list=google_text_data+tweets_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleanedList = [x for x in final_list if str(x) != 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.summarization import summarize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "document='.'.join(cleanedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "DOCUMENT = re.sub(r'\\n|\\r', ' ', document)\n",
    "DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\n",
    "DOCUMENT = DOCUMENT.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2 months ago · Omdena is a company that uses its expansive community of voluntary engineers to create lasting change through projects that take just two.4 months ago · Omdena is a global platform that uses a bottom-up collaborative model to build AI and ML solutions. People from all over the world come.12 months ago · Omdena and its Covid19 project are displayed on the Nasdaq billboard in . Omdena is an innovation platform for building AI solutions to.4 months ago · A leader in collaborative AI development, Omdena believes that truly great startups should build solutions that positively change the world by.17 months ago · Omdena is a collaborative platform for building AI solutions to real-world problems through the power of bottom up collaboration.13 months ago · Rudradeb Mitra, is the Founder of Omdena a global platform where a community of changemakers building AI solutions to real-world problems.7 months ago · Bambini introduced on Omdena, a global platform where AI experts and data scientists from diverse backgrounds collaborate to create. the African Centre for Economic Transformation (ACET) and Omdena, . ACET and Omdena completed Africa\\'s first ever Artificial Intelligence.12 months ago · –based Omdena, will scour publicly available data sources and apply data visualization and AI tools to investigate how government policies are.11 months ago · Omdena, a platform for building AI solutions through global collaboration, is using Labelbox to annotate the positive or negative effects of.6 months ago · Safecity and Omdena. Formed after the 2012 Nirbhaya Gang-rape in India, this AI model is a collaborative technique by Omdena and Safecity.2 months ago · RE-WORK is a leader in AI and Deep Learning events, organizing . Lead Machine Learning Engineer from Omdena; Harini Suresh, PhD.5 months ago · Many AI-based companies are adapting to these annotations for . Omdena is used for labelling tasks in deep learning for tree identification.4 months ago · Omdena brings together larger groups of AI professionals to solve social problems e., reducing rural family violence during a famine.2 months ago · We teamed up with a company called Omdena that organizes AI for good challenges. We recruited over 50 data scientists in order to work with. diplomacy to pharma clinical trials management to satellite telemetry and AI. Omdena, for example, actively engages platform members in.4 months ago · Fhinck is a software installed on computers that uses artificial intelligence to . Omdena is a global platform that uses a bottom-up collaborative model to build AI.7 months ago · Omdena in India is extending freelancing into AI enabled FAST teaming in the non-profit world, and Spacely here in the US is bringing.1 month ago · Maybe it\\'s a compelling cause which helps Omdena - collaboratively solving social problems in developing countries using AI – attract.13 months ago · One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units.5 months ago · SANSA has also executed a project that uses Satellite Imagery and AI to locate informal settlements. Kenya witnessed an array of partnerships.22 months ago · What is the promise of AI to radiologists? How will the future customize preventive medicine using cross-functional data, like genomics and.8 months ago · In high demand tech areas like AI and machine learning, new digital marketplaces Experfy, Omdena, and Contra, and established platforms.13 months ago · In addition to practicing medicine, he also enjoys programming. His recent research project AIBx uses AI to classify thyroid nodules.4 months ago · Rudradeb Mitra: Rudradeb, Founder of Omdena, oversees all . It monitors developments, recognition, and achievements made by AI, big data.24 months ago · One of the upcoming startups, Omdena, is building an educational platform for AI enthusiasts and students to collaborate with organizations on.13 months ago · One of their partners, Omdena, is calling on AI entrepreneurs to help governments design data-driven policy via its coronavirus challenge.12 months ago · An additional startup with a presence in Mexico is Orion Startups-backed Omdena, which recently partnered with the United Nations\\' AI for.12 months ago · Applying AI to Analyze Domestic Violence in Lockdowns — From No Data to Building an ML Classifier. Harshita Chopra in Omdena.14 months ago · En Turquie, des experts en IA de la plateforme collaborative Omdena AI ont développé un prototype d\\'IA spécialement pensé pour résoudre ce. sztucznej inteligencji i uczenia maszynowego zebranych przez Omdena w ramach \"AI for good challenge\" po to, by przebadać młodych ludzi.13 months ago · Hiervoor gaat Fruitpunch AI samenwerken met Omdena, een wereldwijd AI-platform uit de VS. Ondertussen zitten bijna alle scholieren en.2020 – The AI Journal hat 30 Frauen gefragt, welche Tipps sie anderen Frauen . Marisa Tschopp hat mit der Lern-Plattform Omdena diese und weitere.In order to show you the most relevant results, we have omitted some entries very similar to the 33 already displayed. If you like, you can repeat the search with the omitted results included.RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya\\'s efforts to restore degraded land in an equitable way, using tools inc….RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya\\'s efforts to restore degraded land in an equitable way, using tools inc….RT @neo4j: Omdena AI ( @OmdenaAI) launches a project to boost Kenya\\'s efforts to restore degraded land in an equitable way, using tools inc….Omdena AI ( @OmdenaAI) launches a project to boost Kenya\\'s efforts to restore degraded land in an equitable way, us… https://t.co/iU5RhzIQMF.RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job 11 proven Do\\'s and Don\\'ts you need to know to build yo….RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job 11 proven Do\\'s and Don\\'ts you need to know to build yo….RT @OmdenaAI: How to Build a Data Science Portfolio in 2021 That Gets You a Top Job 11 proven Do\\'s and Don\\'ts you need to know to build yo….How to Build a Data Science Portfolio in 2021 That Gets You a Top Job 11 proven Do\\'s and Don\\'ts you need to know t… https://t.co/iXzEeyjWmP.RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment? #BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor…'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DOCUMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 months ago · Omdena is a company that uses its expansive community of voluntary engineers to create lasting change through projects that take just two.4 months ago · Omdena is a global platform that uses a bottom-up collaborative model to build AI and ML solutions.\n",
      "Omdena is a global platform that uses a bottom-up collaborative model to build AI.7 months ago · Omdena in India is extending freelancing into AI enabled FAST teaming in the non-profit world, and Spacely here in the US is bringing.1 month ago · Maybe it's a compelling cause which helps Omdena - collaboratively solving social problems in developing countries using AI – attract.13 months ago · One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units.5 months ago · SANSA has also executed a project that uses Satellite Imagery and AI to locate informal settlements.\n",
      "#BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment?\n",
      "#BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment?\n",
      "#BigData #Analytics #DataScience #AI #IoT #IIoT #PyTorch #Python #RStats #Tensor….RT @gp_pulipaka: Can AI Help to Prevent Sexual Harassment?\n"
     ]
    }
   ],
   "source": [
    "print(summarize(DOCUMENT, ratio=0.2, split=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Omdena is a global platform that uses a bottom-up collaborative model to build AI.7 months ago · Omdena in India is extending freelancing into AI enabled FAST teaming in the non-profit world, and Spacely here in the US is bringing.1 month ago · Maybe it's a compelling cause which helps Omdena - collaboratively solving social problems in developing countries using AI – attract.13 months ago · One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units.5 months ago · SANSA has also executed a project that uses Satellite Imagery and AI to locate informal settlements.\n"
     ]
    }
   ],
   "source": [
    "print(summarize(DOCUMENT, word_count=75, split=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import nltk\n",
    "\n",
    "sentences = nltk.sent_tokenize(DOCUMENT)\n",
    "len(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "\n",
    "stop_words = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "def normalize_document(doc):\n",
    "    # lower case and remove special characters\\whitespaces\n",
    "    doc = re.sub(r'[^a-zA-Z\\s]', '', doc, re.I|re.A)\n",
    "    doc = doc.lower()\n",
    "    doc = doc.strip()\n",
    "    # tokenize document\n",
    "    tokens = nltk.word_tokenize(doc)\n",
    "    # filter stopwords out of document\n",
    "    filtered_tokens = [token for token in tokens if token not in stop_words]\n",
    "    # re-create document from filtered tokens\n",
    "    doc = ' '.join(filtered_tokens)\n",
    "    return doc\n",
    "\n",
    "normalize_corpus = np.vectorize(normalize_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['months ago omdena company uses expansive community voluntary engineers create lasting change projects take two months ago omdena global platform uses bottomup collaborative model build ai ml solutions',\n",
       "       'people world come months ago omdena covid project displayed nasdaq billboard',\n",
       "       'omdena innovation platform building ai solutions months ago leader collaborative ai development omdena believes truly great startups build solutions positively change world months ago omdena collaborative platform building ai solutions realworld problems power bottom collaboration months ago rudradeb mitra founder omdena global platform community changemakers building ai solutions realworld problems months ago bambini introduced omdena global platform ai experts data scientists diverse backgrounds collaborate create'],\n",
       "      dtype='<U909')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_sentences = normalize_corpus(sentences)\n",
    "norm_sentences[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We found that the this gensim method seems the best method and gives better summarization in this case"
   ]
  }
 ],
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