Political_Likeability.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thuhinkhanna/PoliticalLikeability/blob/main/Political_Likeability.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Collection Script\n"
      ],
      "metadata": {
        "id": "WjJYdmvjhrGo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import praw\n",
        "import sys\n",
        "from praw.models import MoreComments\n",
        "import pandas as pd\n",
        "from pymongo import MongoClient\n",
        "# OAuth\n",
        "reddit = praw.Reddit( \n",
        "        client_id=\"Z8chjFnnwYysVXaVGU1SNw\", \n",
        "        client_secret=\"7UHH7gdjAtV8dZXIN16-Eo-_8x4lrQ\", \n",
        "        password=\"crazybasuke\", \n",
        "        user_agent=\"coursesearchbot by u/coursebotadmin\", \n",
        "        username=\"coursebotadmin\" )\n",
        "leaders = [\n",
        "        'Narendra Modi', 'Donald Trump', 'Joe Biden', 'Rahul Gandhi', 'Jacinda Ardern', 'Justin Trudeau', 'Boris Johnson', 'Scott Morrison', 'Xi Jinping', 'Mette Frederiksen',\n",
        "        'Sanna Marin', 'Emmanuel Macron', 'Olaf Scholz', 'Naftali Bennett', 'Fumio Kishida', 'Alihan Smaiylov', 'Uhuru Kenyatta', 'Muhammadu Buhari', 'Kim Jong-un', \n",
        "        'Haitham bin Tariq', 'Shehbaz Sharif', 'Imran Khan', 'Mahmoud Abbas', 'Bongbong Marcus', 'Vladimir Putin', 'Salman bin Abdulaziz Al Saud', 'Macky Sall', \n",
        "        'Lee Hsien Loong', 'Mohamed Hussien Roble', 'Mahinda Rajapaksa', 'Gotabaya Rajapaksa', 'Salva Kiir Mayardit', 'Yoon Suk-yeol', 'Cyril Ramaphosa', \n",
        "        'Abdel Fattah al-Burhan', 'Magdalena Andersson', 'Ignazio Cassis', 'Bashar al-Assad', 'Emmerson Mnangagwa', 'Sheikh Khalifa bin Zayed Al Nahyan', 'Volodymyr Zelenskyy',\n",
        "        'Abraham Lincoln', 'Nelson Mandela', 'A.P.J. Abdul Kalam', 'Kofi Annan', 'Aung San Suu Kyi', 'Atal Bihari Vajpayee', 'Jawaharlal Nehru', 'Robert Mugabe', 'Ashraf Ghani'\n",
        "        ]\n",
        "#for leader in leaders :\n",
        "for i in range(50):\n",
        "    leader = leaders[i]\n",
        "    leader_document = {}\n",
        "    results = reddit.subreddit(\"all\").search(leader)\n",
        "    leader_document[\"name\"] = leader\n",
        "    # Result has a set of post ids\n",
        "    posts=[]\n",
        "    for post_id in results:\n",
        "        posts.append(post_id)\n",
        "    n_posts = 10\n",
        "    if(len(posts)<10):\n",
        "        n_posts = len(posts)\n",
        "    # Extracting Comments\n",
        "    for x in range(n_posts):\n",
        "        j=1\n",
        "        post_document = {}\n",
        "        post_document[\"title\"]=posts[x].title\n",
        "        posts[x].comments.replace_more(limit=0)\n",
        "        cmnt_lst = posts[x].comments.list();\n",
        "        print(cmnt_lst)\n",
        "        for comment in cmnt_lst:\n",
        "            key = \"cmnt\"+str(j)\n",
        "            post_document[key]=str(comment.body)\n",
        "            j+=1\n",
        "        post_key = \"post\"+str(x)\n",
        "        leader_document[post_key]=post_document\n",
        "    # Connection to MongoDB\n",
        "    client = MongoClient(\"mongodb+srv://l3admin:JZlYHY7fumfjrjQg@l3dataset.v5xlc.mongodb.net/myFirstDatabase?retryWrites=true&w=majority\")\n",
        "    db = client.test\n",
        "    db = client['L3Dataset']\n",
        "    coll = db['Leaders']\n",
        "    # Adding document to collection\n",
        "    coll.insert_one(leader_document)\n",
        "    "
      ],
      "metadata": {
        "id": "e610c5c2hpGB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dependencies"
      ],
      "metadata": {
        "id": "7hc42z28hwWk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HZmTnFuOTN7C"
      },
      "outputs": [],
      "source": [
        "!pip install praw\n",
        "!pip install transformers\n",
        "!pip install pymongo\n",
        "!pip install dnspython\n",
        "!pip install langdetect\n",
        "!pip3 install pymongo[srv]\n",
        "!pip3 install pymongo[tls]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dKMRKRc3TSws"
      },
      "outputs": [],
      "source": [
        "import praw\n",
        "import sys\n",
        "from praw.models import MoreComments\n",
        "import pandas as pd\n",
        "from pymongo import MongoClient\n",
        "import pymongo\n",
        "from transformers import pipeline\n",
        "from langdetect import detect, LangDetectException"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Retrieval"
      ],
      "metadata": {
        "id": "WIvbKfVZh1ol"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h8bZNQGxrj8y"
      },
      "outputs": [],
      "source": [
        "client = MongoClient(\"mongodb+srv://l3admin:JZlYHY7fumfjrjQg@l3dataset.v5xlc.mongodb.net/myFirstDatabase?retryWrites=true&w=majority\")\n",
        "db = client.tes\n",
        "db = client['L3Dataset']\n",
        "coll = db['Leaders']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BwnshhMvFGDo"
      },
      "outputs": [],
      "source": [
        "leaders = [\"Narendra Modi\", \"Donald Trump\", \"Joe Biden\", \"Rahul Gandhi\", \"Jacinda Ardern\", \"Justin Trudeau\", \"Boris Johnson\", \"Scott Morrison\", \"Xi Jinping\", \"Mette Frederiksen\", \"Sanna Marin\", \"Emmanuel Macron\", \"Olaf Scholz\", \"Naftali Bennett\", \"Fumio Kishida\", \"Uhuru Kenyatta\", \"Muhammadu Buhari\", \"Kim Jong-un\", \"Shehbaz Sharif\", \"Imran Khan\", \"Mahmoud Abbas\", \"Vladimir Putin\", \"Lee Hsien Loong\", \"Mahinda Rajapaksa\", \"Bashar al-Assad\", \"Volodymyr Zelenskyy\", \"Abraham Lincoln\", \"Nelson Mandela\", \"Ashraf Ghani\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AUGJqwBub3UO"
      },
      "outputs": [],
      "source": [
        "# Data Cleaning\n",
        "leader_posts = coll.find_one({'name':'Narendra Modi'})\n",
        "cmnt_lst = []\n",
        "for i in range(2,12):\n",
        "  post=leader_posts[str(\"post\"+str(i-2))]\n",
        "  m = len(post)\n",
        "  for j in range(1,m):\n",
        "    cmnt = post[str(\"cmnt\"+str(j))]\n",
        "    if(len(cmnt)>10):\n",
        "      if((\"award\" not in cmnt) and (\"u/\" not in cmnt) and (\"removed\" not in cmnt) and (\"deleted\" not in cmnt) and (\"r/\" not in cmnt) and (\"https\" not in cmnt) and (\"http\" not in cmnt) and (detect(cmnt)==\"en\")):\n",
        "        cmnt_lst.append(cmnt[:100])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2PDUI-YxuAkX"
      },
      "source": [
        "# Transformers with PyTorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
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        },
        "id": "DCC7SA5TmG8C",
        "outputId": "d39f63f1-0544-40a6-f176-80073b902d62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
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              "model_id": "bdfe9a5b537140c1a2e4fce516f09573"
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          "metadata": {}
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        {
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              "model_id": "2e0220832b934bfc86466f4b63229671"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "sentiment_analysis = pipeline(\"sentiment-analysis\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_yDnjgtnCLvV"
      },
      "outputs": [],
      "source": [
        "for i in cmnt_lst:\n",
        "  result = sentiment_analysis(i)[0]\n",
        "  print(\"Label:\", result['label'])\n",
        "  print(\"Confidence Score:\", result['score'])\n",
        "\n",
        "# result = sentiment_analysis(neg_text)[0]\n",
        "# print(\"Label:\", result['label'])\n",
        "# print(\"Confidence Score:\", result['score'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MVeAPaR0vD3H"
      },
      "outputs": [],
      "source": [
        "neg_text = \"God damn it where is the fucking free award when you need it\"\n",
        "\n",
        "pos_text = \"Genius doesn’t typically get packaged inside square boxes and I’m cool with that personally.\"\n",
        "\n",
        "random_text = \"I'm not very happy\"\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tzg97ibkvLkY",
        "outputId": "fa464488-2d73-48eb-dc77-e0e7d98b59dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: NEGATIVE\n",
            "Confidence Score: 0.9977850914001465\n"
          ]
        }
      ],
      "source": [
        "result = sentiment_analysis(neg_text)[0]\n",
        "print(\"Label:\", result['label'])\n",
        "print(\"Confidence Score:\", result['score'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twNxsqwLvctN",
        "outputId": "837210ac-4cf0-46d4-bca2-4637c1c2845e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label: POSITIVE\n",
            "Confidence Score: 0.9997612833976746\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result = sentiment_analysis(pos_text)[0]\n",
        "print(\"Label:\", result['label'])\n",
        "print(\"Confidence Score:\", result['score'])\n",
        "print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M5_9Op1tv8NX"
      },
      "outputs": [],
      "source": [
        "positive_lst = []\n",
        "leaders_total = []\n",
        "leaders_scores = []\n",
        "def Transformers_Calculator(leaders):  \n",
        "  for l in leaders:\n",
        "    print(l)\n",
        "    leaders_posts = coll.find_one({'name': l})\n",
        "    clst = []\n",
        "    for i in range(2,12):\n",
        "      post=leaders_posts[str(\"post\"+str(i-2))]\n",
        "      m = len(post)\n",
        "      for j in range(1,m):\n",
        "        cmnt = post[str(\"cmnt\"+str(j))]\n",
        "        if(len(cmnt)>10):\n",
        "          if((\"award\" not in cmnt) and (\"u/\" not in cmnt) and (\"removed\" not in cmnt) and (\"deleted\" not in cmnt) and (\"r/\" not in cmnt) and (\"https\" not in cmnt) and (\"http\" not in cmnt)):\n",
        "            try:\n",
        "              if((detect(cmnt)==\"en\")):\n",
        "                clst.append(cmnt[:100])\n",
        "            except LangDetectException:\n",
        "              continue\n",
        "    res = []\n",
        "    pos = []\n",
        "    for x in clst:\n",
        "      res.append(sentiment_analysis(x)[0])\n",
        "      \n",
        "    score = 0\n",
        "    for i in res:\n",
        "      if (i['label'] == \"POSITIVE\"):\n",
        "        score = score + 1\n",
        "        #print(\"Hi\")\n",
        "    \n",
        "    leaders_scores.append(score)    \n",
        "    leaders_total.append(len(res))\n",
        "    \n",
        "    \n",
        "\n",
        "    # score = 0\n",
        "    # for pol in res:\n",
        "    #   if pol>=0.0 :\n",
        "    #     score = score + 1\n",
        "    # leaders_scores.append(score)\n",
        "    # leaders_total.append(len(res))\n",
        "\n",
        "#     TBresults = {}\n",
        "# for i in range(29):\n",
        "#   TBresults[str(leaders[i])]=(leaders_scores[i]/leaders_total[i])*100\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fMkEFeTkAcW8",
        "outputId": "9feeb632-3564-4b10-a067-e8f59be878fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Narendra Modi\n",
            "Donald Trump\n",
            "Joe Biden\n",
            "Rahul Gandhi\n",
            "Jacinda Ardern\n",
            "Justin Trudeau\n",
            "Boris Johnson\n",
            "Scott Morrison\n",
            "Xi Jinping\n",
            "Mette Frederiksen\n",
            "Sanna Marin\n",
            "Emmanuel Macron\n",
            "Olaf Scholz\n",
            "Naftali Bennett\n",
            "Fumio Kishida\n",
            "Uhuru Kenyatta\n",
            "Muhammadu Buhari\n",
            "Kim Jong-un\n",
            "Shehbaz Sharif\n",
            "Imran Khan\n",
            "Mahmoud Abbas\n",
            "Vladimir Putin\n",
            "Lee Hsien Loong\n",
            "Mahinda Rajapaksa\n",
            "Bashar al-Assad\n",
            "Volodymyr Zelenskyy\n",
            "Abraham Lincoln\n",
            "Nelson Mandela\n",
            "Ashraf Ghani\n"
          ]
        }
      ],
      "source": [
        "Transformers_Calculator(leaders)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jdWkREXeAP8u"
      },
      "outputs": [],
      "source": [
        "TBresults = {}\n",
        "\n",
        "for i in range(29):\n",
        "  TBresults[str(leaders[i])]=(leaders_scores[i]/leaders_total[i])*100"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(leaders_scores)\n",
        "print(leaders_total)"
      ],
      "metadata": {
        "id": "xJviXI0sWIfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(TBresults)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvJNGmWsWQFr",
        "outputId": "65e392af-8418-4c72-badf-77bf110a8253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Narendra Modi': 0.5181347150259068, 'Donald Trump': 0.18298261665141813, 'Joe Biden': 0.13049151805132667, 'Rahul Gandhi': 0.16611295681063123, 'Jacinda Ardern': 0.15451174289245984, 'Justin Trudeau': 0.13956734124214934, 'Boris Johnson': 0.1532399299474606, 'Scott Morrison': 0.1442741208295762, 'Xi Jinping': 0.14539579967689822, 'Mette Frederiksen': 0.1601794009290405, 'Sanna Marin': 0.1656127672387835, 'Emmanuel Macron': 0.16576875259013676, 'Olaf Scholz': 0.17125543406665789, 'Naftali Bennett': 0.18307833137177978, 'Fumio Kishida': 0.18898828272647095, 'Uhuru Kenyatta': 0.20118194392053312, 'Muhammadu Buhari': 0.21372894141312546, 'Kim Jong-un': 0.21015761821366027, 'Shehbaz Sharif': 0.21907067911910524, 'Imran Khan': 0.2217049107637734, 'Mahmoud Abbas': 0.2303894679100384, 'Vladimir Putin': 0.21801605390942425, 'Lee Hsien Loong': 0.22153727605471008, 'Mahinda Rajapaksa': 0.22878932316491898, 'Bashar al-Assad': 0.2348961758902565, 'Volodymyr Zelenskyy': 0.2216538789428815, 'Abraham Lincoln': 0.22262532981530345, 'Nelson Mandela': 0.22450288646568314, 'Ashraf Ghani': 0.22840040954556196}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bxb2dgXiq_Lw"
      },
      "outputs": [],
      "source": [
        "!pip install TextBlob\n",
        "#python3 -m textblob.download_corpora\n",
        "from textblob import TextBlob\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6coleCJNqGCG"
      },
      "outputs": [],
      "source": [
        "analysis = TextBlob(self.clean_tweet(tweet)) \n",
        "        # set sentiment \n",
        "if analysis.sentiment.polarity > 0: \n",
        "  return 'positive'\n",
        "elif analysis.sentiment.polarity == 0: \n",
        "  return 'neutral'\n",
        "else: \n",
        "  return 'negative'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R4ZdElA4qGE1"
      },
      "outputs": [],
      "source": [
        "analysis = TextBlob(\"I'm not very happy\")\n",
        "\n",
        "print(analysis.sentiment.polarity)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Si_B5bMAq_I7"
      },
      "outputs": [],
      "source": [
        "analysis = TextBlob(\"Edit: Thank you for the awards. I’m happy to have this be by far my best received contribution to Reddit.\")\n",
        "print(analysis.sentiment.polarity)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2qGo23poGJ8c"
      },
      "source": [
        "# Jacinda Ardern using TextBlob Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYlp6s_Az5o_",
        "outputId": "ae8f9b98-b15c-4138-c7a4-e178a2a9a8a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: TextBlob in /usr/local/lib/python3.7/dist-packages (0.15.3)\n",
            "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.7/dist-packages (from TextBlob) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk>=3.1->TextBlob) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install TextBlob\n",
        "from textblob import TextBlob\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8bRkQ7j_H0c",
        "outputId": "82cd5628-d527-41f3-f4dc-27cedaf0a205"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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-0.05, -0.55, -0.03333333333333333, 0.125, 0.0, 0.25, 0.0, -0.05, 0.0, 0.31666666666666665, 0.13636363636363635, -0.05, 0.0, 0.0, 0.0, 0.0, -0.09999999999999999, 0.2777777777777778, -0.2, 0.0, 0.25, -0.3125, 0.14583333333333334, -0.3, -0.7999999999999999, 0.0, 0.0, 0.0, -0.18125, 0.0, -0.14583333333333334, 0.1875, 0.0, 0.15, 0.0, 0.1361111111111111, 0.0, 0.0, 0.475, 0.0, 0.2, -0.2, 0.040178571428571425, 0.0, 0.5, -0.3125, 0.0, -0.30833333333333335, 0.0, 0.0, 0.17857142857142858, 0.0, -0.4, -0.25, 0.0, -0.5333333333333333, 0.0, 0.0, 0.0, 0.225, 0.040178571428571425, -0.10714285714285715, 0.0, -0.5, -0.2, -0.125, 0.0, 0.0, 0.0, 0.18614718614718614, 0.0, 0.09999999999999999, 0.19999999999999998, 0.0, 0.5375, 0.2375, -0.1, -0.15000000000000002, 0.32857142857142857, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.25, -0.75, -0.007142857142857145, 0.0, 0.7000000000000001, 0.7000000000000001, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, -0.4, 0.12857142857142856, 0.0, 0.10000000000000002, 0.10000000000000002, -0.1, 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          ]
        }
      ],
      "source": [
        "final_lst = []\n",
        "for i in cmnt_lst:\n",
        "  analysis = TextBlob(i)\n",
        "  polarity = analysis.sentiment.polarity\n",
        "  final_lst.append(polarity)\n",
        "\n",
        "print(final_lst)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J3vEU1lYDqPg"
      },
      "outputs": [],
      "source": [
        "SCORE = 0\n",
        "for i in final_lst:\n",
        "  if(i>0.0):\n",
        "    SCORE = SCORE + 1\n",
        "print(SCORE)\n",
        "print(len(pols))\n",
        "print((SCORE/len(pols))*100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCJ-PN3CwXXp"
      },
      "source": [
        "# TextBlob Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4dbkKqcwh5G"
      },
      "outputs": [],
      "source": [
        "!pip install TextBlobx\n",
        "\n",
        "from textblob import TextBlob\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "slEZXSJY3zS1"
      },
      "outputs": [],
      "source": [
        "leaders_total = []\n",
        "leaders_scores = []\n",
        "def TB_Calculator(leaders):  \n",
        "  for l in leaders:\n",
        "    print(l)\n",
        "    leaders_posts = coll.find_one({'name': l})\n",
        "    clst = []\n",
        "    for i in range(2,12):\n",
        "      post=leaders_posts[str(\"post\"+str(i-2))]\n",
        "      m = len(post)\n",
        "      for j in range(1,m):\n",
        "        cmnt = post[str(\"cmnt\"+str(j))]\n",
        "        if(len(cmnt)>10):\n",
        "          if((\"award\" not in cmnt) and (\"u/\" not in cmnt) and (\"removed\" not in cmnt) and (\"deleted\" not in cmnt) and (\"r/\" not in cmnt) and (\"https\" not in cmnt) and (\"http\" not in cmnt)):\n",
        "            try:\n",
        "              if((detect(cmnt)==\"en\")):\n",
        "                clst.append(cmnt[:100])\n",
        "            except LangDetectException:\n",
        "              continue\n",
        "    res = []\n",
        "    for x in clst:\n",
        "      res.append(TextBlob(x).sentiment.polarity)\n",
        "    score = 0\n",
        "    for pol in res:\n",
        "      if pol>=0.0 :\n",
        "        score = score + 1\n",
        "    leaders_scores.append(score)\n",
        "    leaders_total.append(len(res))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-SGB_PC43Qq",
        "outputId": "ab4209b5-f946-4301-c690-1b794756fb56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Narendra Modi\n",
            "Donald Trump\n",
            "Joe Biden\n",
            "Rahul Gandhi\n",
            "Jacinda Ardern\n",
            "Justin Trudeau\n",
            "Boris Johnson\n",
            "Scott Morrison\n",
            "Xi Jinping\n",
            "Mette Frederiksen\n",
            "Sanna Marin\n",
            "Emmanuel Macron\n",
            "Olaf Scholz\n",
            "Naftali Bennett\n",
            "Fumio Kishida\n",
            "Uhuru Kenyatta\n",
            "Muhammadu Buhari\n",
            "Kim Jong-un\n",
            "Shehbaz Sharif\n",
            "Imran Khan\n",
            "Mahmoud Abbas\n",
            "Vladimir Putin\n",
            "Lee Hsien Loong\n",
            "Mahinda Rajapaksa\n",
            "Bashar al-Assad\n",
            "Volodymyr Zelenskyy\n",
            "Abraham Lincoln\n",
            "Nelson Mandela\n",
            "Ashraf Ghani\n"
          ]
        }
      ],
      "source": [
        "# runs the whole thing - 5 mins\n",
        "TB_Calculator(leaders)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkKqVYIx46KR",
        "outputId": "49299dad-3e4f-4497-f1f5-bfd8da1293c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[565, 2261, 3186, 356, 1971, 2851, 826, 2621, 1473, 123, 1023, 1463, 856, 151, 661, 57, 3, 1531, 392, 1037, 317, 2716, 750, 270, 485, 2191, 911, 827, 789]\n",
            "[714, 3083, 4225, 462, 2581, 3681, 1113, 3719, 1943, 168, 1284, 1914, 1168, 187, 817, 75, 4, 1971, 512, 1350, 401, 3647, 930, 350, 602, 2805, 1156, 1091, 973]\n"
          ]
        }
      ],
      "source": [
        "print(leaders_scores)\n",
        "print(leaders_total)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ceCyJOm-4_Nb"
      },
      "outputs": [],
      "source": [
        "TBresults = {}\n",
        "for i in range(29):\n",
        "  TBresults[str(leaders[i])]=(leaders_scores[i]/leaders_total[i])*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBLBkWIW5CWR",
        "outputId": "2deb6138-6f14-432b-d08a-6b601ef235a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'Narendra Modi': 79.30069930069929, 'Donald Trump': 73.30741820537739, 'Joe Biden': 75.36163149158169, 'Rahul Gandhi': 76.97228144989339, 'Jacinda Ardern': 76.4614789005033, 'Justin Trudeau': 77.3764258555133, 'Boris Johnson': 74.21383647798741, 'Scott Morrison': 70.46673819742489, 'Xi Jinping': 75.98971722365039, 'Mette Frederiksen': 73.21428571428571, 'Sanna Marin': 79.64257964257965, 'Emmanuel Macron': 76.46750524109015, 'Olaf Scholz': 73.24786324786324, 'Naftali Bennett': 80.74866310160428, 'Fumio Kishida': 80.88235294117648, 'Uhuru Kenyatta': 77.02702702702703, 'Muhammadu Buhari': 75.0, 'Kim Jong-un': 77.6595744680851, 'Shehbaz Sharif': 76.41325536062378, 'Imran Khan': 76.88330871491877, 'Mahmoud Abbas': 78.94736842105263, 'Vladimir Putin': 74.34138309549945, 'Lee Hsien Loong': 80.47210300429185, 'Mahinda Rajapaksa': 77.07736389684814, 'Bashar al-Assad': 80.99173553719008, 'Volodymyr Zelenskyy': 78.07048771804914, 'Abraham Lincoln': 78.80622837370242, 'Nelson Mandela': 75.8494031221304, 'Ashraf Ghani': 81.27572016460906}\n"
          ]
        }
      ],
      "source": [
        "print(TBresults)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vE-iOcaCGfjM"
      },
      "source": [
        "# Jacinda Ardern with BERT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PJ7IT6VBGe0F"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "\n",
        "example_text = 'God damn it where is the fucking free award when you need it'\n",
        "bert_input = tokenizer(example_text,padding='max_length', max_length = 25, \n",
        "                       truncation=True, return_tensors=\"pt\")\n",
        "\n",
        "\n",
        "print(bert_input['input_ids'])\n",
        "print(bert_input['token_type_ids'])\n",
        "print(bert_input['attention_mask'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eLws6WL-Ge39"
      },
      "outputs": [],
      "source": [
        "\n",
        "example_text = tokenizer.decode(bert_input.input_ids[0])\n",
        "\n",
        "print(example_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZnvbbgQQGe_Q"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "from transformers import BertTokenizer\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
        "labels = {'business':0,\n",
        "          'entertainment':1,\n",
        "          'sport':2,\n",
        "          'tech':3,\n",
        "          'politics':4\n",
        "          }\n",
        "\n",
        "class Dataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, df):\n",
        "\n",
        "        self.labels = [labels[label] for label in df['category']]\n",
        "        self.texts = [tokenizer(text, \n",
        "                               padding='max_length', max_length = 512, truncation=True,\n",
        "                                return_tensors=\"pt\") for text in df['text']]\n",
        "\n",
        "    def classes(self):\n",
        "        return self.labels\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def get_batch_labels(self, idx):\n",
        "        # Fetch a batch of labels\n",
        "        return np.array(self.labels[idx])\n",
        "\n",
        "    def get_batch_texts(self, idx):\n",
        "        # Fetch a batch of inputs\n",
        "        return self.texts[idx]\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "\n",
        "        batch_texts = self.get_batch_texts(idx)\n",
        "        batch_y = self.get_batch_labels(idx)\n",
        "\n",
        "        return batch_texts, batch_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7eD-c0ZkJnUl"
      },
      "outputs": [],
      "source": [
        "from torch.optim import Adam\n",
        "from tqdm import tqdm\n",
        "\n",
        "def train(model, train_data, val_data, learning_rate, epochs):\n",
        "\n",
        "    train, val = Dataset(train_data), Dataset(val_data)\n",
        "\n",
        "    train_dataloader = torch.utils.data.DataLoader(train, batch_size=2, shuffle=True)\n",
        "    val_dataloader = torch.utils.data.DataLoader(val, batch_size=2)\n",
        "\n",
        "    use_cuda = torch.cuda.is_available()\n",
        "    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr= learning_rate)\n",
        "\n",
        "    if use_cuda:\n",
        "\n",
        "            model = model.cuda()\n",
        "            criterion = criterion.cuda()\n",
        "\n",
        "    for epoch_num in range(epochs):\n",
        "\n",
        "            total_acc_train = 0\n",
        "            total_loss_train = 0\n",
        "\n",
        "            for train_input, train_label in tqdm(train_dataloader):\n",
        "\n",
        "                train_label = train_label.to(device)\n",
        "                mask = train_input['attention_mask'].to(device)\n",
        "                input_id = train_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                output = model(input_id, mask)\n",
        "                \n",
        "                batch_loss = criterion(output, train_label)\n",
        "                total_loss_train += batch_loss.item()\n",
        "                \n",
        "                acc = (output.argmax(dim=1) == train_label).sum().item()\n",
        "                total_acc_train += acc\n",
        "\n",
        "                model.zero_grad()\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            \n",
        "            total_acc_val = 0\n",
        "            total_loss_val = 0\n",
        "\n",
        "            with torch.no_grad():\n",
        "\n",
        "                for val_input, val_label in val_dataloader:\n",
        "\n",
        "                    val_label = val_label.to(device)\n",
        "                    mask = val_input['attention_mask'].to(device)\n",
        "                    input_id = val_input['input_ids'].squeeze(1).to(device)\n",
        "\n",
        "                    output = model(input_id, mask)\n",
        "\n",
        "                    batch_loss = criterion(output, val_label)\n",
        "                    total_loss_val += batch_loss.item()\n",
        "                    \n",
        "                    acc = (output.argmax(dim=1) == val_label).sum().item()\n",
        "                    total_acc_val += acc\n",
        "            \n",
        "            print(\n",
        "                f'Epochs: {epoch_num + 1} | Train Loss: {total_loss_train / len(train_data): .3f} \\\n",
        "                | Train Accuracy: {total_acc_train / len(train_data): .3f} \\\n",
        "                | Val Loss: {total_loss_val / len(val_data): .3f} \\\n",
        "                | Val Accuracy: {total_acc_val / len(val_data): .3f}')\n",
        "                  \n",
        "EPOCHS = 5\n",
        "model = BertClassifier()\n",
        "LR = 1e-6\n",
        "              \n",
        "train(model, df_train, df_val, LR, EPOCHS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IfHwgKN9MFH4"
      },
      "outputs": [],
      "source": [
        "!pip install tensorflow==2.0\n",
        "!pip install tensorflow_hub\n",
        "!pip install bert-for-tf2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hE9f1C46M15T"
      },
      "outputs": [],
      "source": [
        "pip install bert-tensorflow\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CA8L2eiOQgBY"
      },
      "source": [
        "# FLAIR MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CikaXh_EM2mQ"
      },
      "outputs": [],
      "source": [
        "!pip install nltk pymongo praw langdetect flair"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TfPC5nD1mClN"
      },
      "outputs": [],
      "source": [
        "from pymongo import MongoClient\n",
        "from langdetect import detect, LangDetectException"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beMmiPx1tHBC",
        "outputId": "0809bc8d-f877-4fd6-f5ac-962b7189b224"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2022-06-06 13:38:05,973 loading file /root/.flair/models/sentiment-en-mix-distillbert_4.pt\n"
          ]
        }
      ],
      "source": [
        "from flair.models import TextClassifier\n",
        "from flair.data import Sentence\n",
        "sia = TextClassifier.load('en-sentiment')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p2oLFKw3H-TQ"
      },
      "outputs": [],
      "source": [
        "def flair_prediction(x):\n",
        "    sentence = Sentence(x)\n",
        "    sia.predict(sentence)\n",
        "    score = sentence.labels[0]\n",
        "    if \"POSITIVE\" in str(score):\n",
        "        return \"pos\"\n",
        "    elif \"NEGATIVE\" in str(score):\n",
        "        return \"neg\"\n",
        "    else:\n",
        "        return \"neu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PQV7so9yRw5U"
      },
      "outputs": [],
      "source": [
        "def Flair_Score_Calculator(leaders):\n",
        "  total_cmnts = []\n",
        "  leaders_scores = []\n",
        "  for l in leaders:\n",
        "    print(l)\n",
        "    leaders_posts = coll.find_one({'name': l})\n",
        "    clst = []\n",
        "    for i in range(2,12):\n",
        "      post=leaders_posts[str(\"post\"+str(i-2))]\n",
        "      m = len(post)\n",
        "      for j in range(1,m):\n",
        "        cmnt = post[str(\"cmnt\"+str(j))]\n",
        "        if(len(cmnt)>10):\n",
        "          if((\"award\" not in cmnt) and (\"u/\" not in cmnt) and (\"removed\" not in cmnt) and (\"deleted\" not in cmnt) and (\"r/\" not in cmnt) and (\"https\" not in cmnt) and (\"http\" not in cmnt)):\n",
        "            try:\n",
        "              if((detect(cmnt)==\"en\")):\n",
        "                clst.append(cmnt[:100])\n",
        "            except LangDetectException:\n",
        "              continue\n",
        "    res = []\n",
        "    for x in clst:\n",
        "      res.append(flair_prediction(x))\n",
        "    score = 0\n",
        "    for c in res:\n",
        "      if (c=='pos' or c=='neu'):\n",
        "        score = score + 1\n",
        "    leaders_scores.append(score)\n",
        "    total_cmnts.append(len(res))\n",
        "  return leaders_scores, total_cmnts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9ZqcWrbThiw",
        "outputId": "390b0d27-1abe-472b-b1fe-91996f7c5b12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Narendra Modi\n",
            "Donald Trump\n",
            "Joe Biden\n",
            "Rahul Gandhi\n",
            "Jacinda Ardern\n",
            "Justin Trudeau\n",
            "Boris Johnson\n",
            "Scott Morrison\n",
            "Xi Jinping\n",
            "Mette Frederiksen\n",
            "Sanna Marin\n",
            "Emmanuel Macron\n",
            "Olaf Scholz\n",
            "Naftali Bennett\n",
            "Fumio Kishida\n",
            "Uhuru Kenyatta\n",
            "Muhammadu Buhari\n",
            "Kim Jong-un\n",
            "Shehbaz Sharif\n",
            "Imran Khan\n",
            "Mahmoud Abbas\n",
            "Vladimir Putin\n",
            "Lee Hsien Loong\n",
            "Mahinda Rajapaksa\n",
            "Bashar al-Assad\n",
            "Volodymyr Zelenskyy\n",
            "Abraham Lincoln\n",
            "Nelson Mandela\n",
            "Ashraf Ghani\n"
          ]
        }
      ],
      "source": [
        "# runs the whole thing - 8 mins with GPU runtime 43 mins with CPU runtime\n",
        "scores_list, totals_list = Flair_Score_Calculator(leaders)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXULRU2skajw",
        "outputId": "17504e9a-1af3-4371-96c1-55bea9c4c4d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[226, 952, 1350, 125, 919, 1210, 301, 1189, 635, 58, 432, 636, 365, 55, 293, 16, 1, 727, 119, 379, 102, 1085, 351, 119, 154, 1190, 432, 381, 245]\n",
            "[711, 3084, 4234, 467, 2578, 3676, 1112, 3720, 1939, 168, 1286, 1908, 1166, 186, 814, 74, 4, 1977, 513, 1345, 399, 3648, 933, 349, 607, 2802, 1154, 1084, 972]\n"
          ]
        }
      ],
      "source": [
        "print(scores_list)\n",
        "print(totals_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nfDLrslml86b"
      },
      "outputs": [],
      "source": [
        "results = {}\n",
        "for i in range(29):\n",
        "  results[str(leaders[i])]=(scores_list[i]/totals_list[i])*100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3FRLhuDmWI4",
        "outputId": "9849b32d-75c5-4570-d779-223082414901"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'Narendra Modi': 31.78621659634318, 'Donald Trump': 30.86900129701686, 'Joe Biden': 31.884742560226737, 'Rahul Gandhi': 26.76659528907923, 'Jacinda Ardern': 35.6477889837083, 'Justin Trudeau': 32.91621327529924, 'Boris Johnson': 27.068345323741006, 'Scott Morrison': 31.96236559139785, 'Xi Jinping': 32.74883960804538, 'Mette Frederiksen': 34.523809523809526, 'Sanna Marin': 33.592534992223946, 'Emmanuel Macron': 33.33333333333333, 'Olaf Scholz': 31.30360205831904, 'Naftali Bennett': 29.56989247311828, 'Fumio Kishida': 35.995085995085994, 'Uhuru Kenyatta': 21.62162162162162, 'Muhammadu Buhari': 25.0, 'Kim Jong-un': 36.77288821446636, 'Shehbaz Sharif': 23.196881091617932, 'Imran Khan': 28.178438661710036, 'Mahmoud Abbas': 25.563909774436087, 'Vladimir Putin': 29.74232456140351, 'Lee Hsien Loong': 37.62057877813505, 'Mahinda Rajapaksa': 34.097421203438394, 'Bashar al-Assad': 25.370675453047774, 'Volodymyr Zelenskyy': 42.46966452533905, 'Abraham Lincoln': 37.43500866551127, 'Nelson Mandela': 35.14760147601476, 'Ashraf Ghani': 25.205761316872426}\n"
          ]
        }
      ],
      "source": [
        "print(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# BERT"
      ],
      "metadata": {
        "id": "HZDbL6dLgt04"
      }
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        {
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          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
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      ],
      "source": [
        "\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "\n",
        "model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "outputId": "53bae595-45bf-4d20-8774-4ca7dfe8ac85"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"tf_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  109482240 \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,483,778\n",
            "Trainable params: 109,483,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()\n"
      ]
    }
  ],
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    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "vE-iOcaCGfjM"
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      "include_colab_link": true
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