Data_Cleaning_and_LDA.ipynb

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      "name": "Copy of Final_Project.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zONwCBgNWGpz"
      },
      "source": [
        "!pip install pyLDAvis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlJZMVty2hk8"
      },
      "source": [
        "!pip install spacy==2.2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9EtCDIlB1-x"
      },
      "source": [
        "import os\n",
        "os.kill(os.getpid(), 9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzXrL8_F1u5M"
      },
      "source": [
        "## Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2PIesKe0H2j"
      },
      "source": [
        "import os\n",
        "import csv"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRm4CYu90H2y",
        "outputId": "4fc0df7a-4cc5-44c3-da24-f96811598a78"
      },
      "source": [
        "import nltk\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "nltk.download('vader_lexicon')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
            "  warnings.warn(\"The twython library has not been installed. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-z7uzxhB1u5N",
        "outputId": "d5ad3878-bf65-487e-c9d4-e0cccb28a6e8"
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk.stem import PorterStemmer"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhDusJRJCAJZ"
      },
      "source": [
        "## Important:\n",
        "\n",
        "This Python notebook is designed to run in Google Colab. When you run the code below, an upload button will appear. Click the upload button, and choose the .csv file that you want to clean and analyze."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "REdB4WMyTKPJ",
        "outputId": "6e3617d1-1d62-4961-a057-e63e557ff6b2"
      },
      "source": [
        "from google.colab import files \n",
        "  \n",
        "uploaded = files.upload()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6708f21d-2068-498b-97cc-d55ccabaefd8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6708f21d-2068-498b-97cc-d55ccabaefd8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving telegram_privacy.csv to telegram_privacy.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6v1ypS5nXA3J"
      },
      "source": [
        "import pandas as pd\n",
        "import io"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGnNM6MHCp-F"
      },
      "source": [
        "## Important:\n",
        "\n",
        "Choose the python code below that match the name of the file that you uploaded above to read the csv file. For example, if you uploaded the telegram_2020.csv file, please use the df = pd.read_csv(io.BytesIO(uploaded['telegram_2020.csv'])) code and ignore the rest."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "JlZSu2OmTZ_G",
        "outputId": "ad8e387e-9693-4c06-9f52-d21befcc9455"
      },
      "source": [
        " \n",
        "df = pd.read_csv(io.BytesIO(uploaded['telegram_2020.csv']))\n",
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>text</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>#telegram paid service registrations are open ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>#Corona Info for #Germany:\\r\\nNew Cases: 9060\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>If you haven't checked out our #ICON #Telegram...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RT @africademics: Launch Alert: Join our new #...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RT @HannaLiubakova: #Belarus Day 120. Belarusi...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "                                                text\n",
              "0  #telegram paid service registrations are open ...\n",
              "1  #Corona Info for #Germany:\\r\\nNew Cases: 9060\\...\n",
              "2  If you haven't checked out our #ICON #Telegram...\n",
              "3  RT @africademics: Launch Alert: Join our new #...\n",
              "4  RT @HannaLiubakova: #Belarus Day 120. Belarusi..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpiJ7ihMOCaV"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['telegram_2021.csv']))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZK7XqXFpOLzh"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['whatsapp_2020.csv']))"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1OmtUI-OVJU"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['whatsapp_2021.csv']))"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaALlJ7LOhLE"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['telegram_privacy.csv']))"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWp-5tRQOp7_"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['whatsapp_privacy.csv']))"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNnXe7ihOr9D"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['whatsapp_feature.csv']))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuoCzCDnOwWU"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['telegram_feature.csv']))"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEx3rfSLOz9C"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['whatsapp.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_IO3Sb_O39J"
      },
      "source": [
        "df = pd.read_csv(io.BytesIO(uploaded['telegram.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rv_88ukDDcVK"
      },
      "source": [
        "## Important:\n",
        "\n",
        "The dataset will be read as the df object. So all the codes below can be used, regardless of which dataset you uploaded."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omAB3Dtk1u5N"
      },
      "source": [
        "X=df['text']\n",
        "stop_words=stopwords.words('english')\n",
        "stemmer=PorterStemmer()\n",
        "import re\n",
        "cleaned_data=[]\n",
        "for i in range(len(X)):\n",
        "    tweet=re.sub('[^a-zA-Z]',' ',X.iloc[i])\n",
        "    tweet=tweet.lower().split()\n",
        "    tweet=[stemmer.stem(word) for word in tweet if (word not in stop_words)]\n",
        "    tweet=' '.join(tweet)\n",
        "    cleaned_data.append(tweet)"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "itZJA7CD1u5N",
        "outputId": "27bb12f0-e95d-4c5a-f304-0f7b012d085f"
      },
      "source": [
        "df = pd.DataFrame(cleaned_data,columns=[\"tweets_cleaned\"])\n",
        "df.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweets_cleaned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>rt itssssp peopl instal telegram whatsapp chan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>research mkm partner find facebook could face ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>rt dharmarajmc best choic whatsappnewpolici te...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>latest privaci daili http co crm zgbxni whatsa...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ownyourprivaci share post start thedatatalk fa...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      tweets_cleaned\n",
              "0  rt itssssp peopl instal telegram whatsapp chan...\n",
              "1  research mkm partner find facebook could face ...\n",
              "2  rt dharmarajmc best choic whatsappnewpolici te...\n",
              "3  latest privaci daili http co crm zgbxni whatsa...\n",
              "4  ownyourprivaci share post start thedatatalk fa..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YY2IB6Ft0H2-"
      },
      "source": [
        "# LDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGV8XPCPc48c"
      },
      "source": [
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "def lemmatizer(text):        \n",
        "    sent = []\n",
        "    doc = nlp(text)\n",
        "    for word in doc:\n",
        "        sent.append(word.lemma_)\n",
        "    return \" \".join(sent)\n",
        "new_tweets_df_clean = pd.DataFrame(df.tweets_cleaned.apply(lambda x: lemmatizer(x)))\n",
        "new_tweets_df_clean['tweets_cleaned'] = new_tweets_df_clean['tweets_cleaned'].str.replace('-PRON-', '')"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5SAZxdjgcZS",
        "outputId": "514794ac-b878-4609-a22a-7ebb1156576d"
      },
      "source": [
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer(\n",
        "analyzer='word',       \n",
        "min_df=3,# minimum required occurences of a word \n",
        "stop_words='english',# remove stop words\n",
        "lowercase=True,# convert all words to lowercase\n",
        "token_pattern='[a-zA-Z0-9]{3,}',# num chars > 3\n",
        "max_features=5000,# max number of unique words\n",
        "                            )\n",
        "data_matrix = vectorizer.fit_transform(new_tweets_df_clean.tweets_cleaned)\n",
        "data_matrix"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<394x413 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 3501 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjrZoIkrg2TJ"
      },
      "source": [
        "lda_model = LatentDirichletAllocation(\n",
        "n_components=10, # Number of topics\n",
        "learning_method='online',\n",
        "random_state=20,       \n",
        "n_jobs = -1  # Use all available CPUs\n",
        "                                     )\n",
        "lda_output = lda_model.fit_transform(data_matrix)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UJpQG8pkg_Jw",
        "outputId": "56fa87d9-9c04-40e8-a4eb-1db3e5be510b"
      },
      "source": [
        "import pyLDAvis\n",
        "import pyLDAvis.sklearn\n",
        "pyLDAvis.enable_notebook()\n",
        "pyLDAvis.sklearn.prepare(lda_model, data_matrix, vectorizer, mds='tsne')"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/pairwise.py:56: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  dtype = np.float\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:348: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  error = np.finfo(np.float).max\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:349: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  best_error = np.finfo(np.float).max\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:348: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  error = np.finfo(np.float).max\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:349: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  best_error = np.finfo(np.float).max\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el156140239332870480252670572\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el156140239332870480252670572_data = {\"mdsDat\": {\"x\": [32.42095184326172, 0.648955225944519, 71.01968383789062, 3.7859814167022705, -42.666500091552734, -3.6216893196105957, -3.632401704788208, -36.58440017700195, 44.391380310058594, 40.235809326171875], \"y\": [8.115229606628418, 71.70709991455078, 9.971006393432617, 33.18513488769531, -9.22206974029541, -43.52651596069336, -4.171707630157471, 35.804412841796875, 50.61538314819336, -31.591665267944336], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [30.745611887484536, 23.851114600813496, 13.114677791794174, 6.961831261303726, 5.739107974106623, 5.70068738609962, 3.976992785784734, 3.931314332170372, 3.517725847285294, 2.460936133157421]}, \"tinfo\": {\"Term\": [\"telegram\", \"amp\", \"http\", \"privaci\", \"polici\", \"whatsapp\", \"infosec\", \"cybersecur\", \"term\", \"app\", \"justic\", \"updat\", \"facebook\", \"total\", \"databreach\", \"secur\", \"becom\", \"gdpr\", \"peopl\", \"new\", \"malwar\", \"dataprotect\", \"everyon\", \"encrochat\", \"agre\", \"internet\", \"cloud\", \"servic\", \"data\", \"share\", \"whatsappnewpolici\", \"signal\", \"use\", \"light\", \"signalapp\", \"join\", \"news\", \"messag\", \"whatsappprivacypolici\", \"telegram\", \"channel\", \"bitcoin\", \"set\", \"issu\", \"develop\", \"websit\", \"autom\", \"btc\", \"know\", \"eth\", \"differ\", \"crypto\", \"android\", \"action\", \"remov\", \"invest\", \"zec\", \"late\", \"dash\", \"tweet\", \"code\", \"say\", \"free\", \"whatsapp\", \"encrypt\", \"technolog\", \"app\", \"http\", \"like\", \"privaci\", \"plea\", \"peopl\", \"facebook\", \"user\", \"amp\", \"new\", \"secur\", \"help\", \"commun\", \"time\", \"cyber\", \"cyberattack\", \"home\", \"socialmedia\", \"onlin\", \"file\", \"talk\", \"gari\", \"violenc\", \"right\", \"hack\", \"public\", \"surveil\", \"dataprivaci\", \"mani\", \"social\", \"drjdrooghaag\", \"collect\", \"year\", 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\"cloudsecur\", \"infosec\", \"dataprotect\", \"cybersecur\", \"http\", \"privaci\", \"user\", \"telegram\", \"corrupt\", \"nca\", \"hear\", \"justic\", \"encrochat\", \"review\", \"man\", \"amp\", \"everyon\", \"mad\", \"amaz\", \"therew\", \"cigarett\", \"blow\", \"becom\", \"zuckerberg\", \"password\", \"effort\", \"individu\", \"anoth\", \"ethic\", \"intern\", \"safeguard\", \"rule\", \"ident\", \"respect\", \"parti\", \"soon\", \"democraci\", \"dataprivacyday\", \"privaci\", \"busi\", \"today\", \"publicsex\", \"exhibitionist\", \"readi\", \"fuw\", \"publicnak\", \"post\", \"locat\", \"day\", \"wish\", \"download\", \"januari\", \"good\", \"allow\", \"track\", \"iphon\", \"platform\", \"write\", \"everi\", \"mind\", \"fun\", \"topic\", \"competit\", \"speech\", \"advertis\", \"happi\", \"click\", \"appl\", \"individu\", \"leav\", \"mean\", \"user\", \"telegram\", \"http\", \"concern\", \"group\", \"connect\", \"attack\", \"peopl\", \"sell\", \"say\", \"movi\", \"instal\", \"itssssp\", \"delet\", \"alreadi\", \"web\", \"februari\", \"download\", \"violat\", \"chang\", \"peopl\", \"come\", \"import\", \"watch\", \"laugh\", \"xtz\", \"major\", \"coin\", \"demand\", \"accept\", \"think\", \"rais\", \"problem\", \"trade\", \"whatsappprivaci\", \"price\", \"aim\", \"encrypt\", \"solut\", \"account\", \"anonym\", \"polici\", \"telegram\", \"whatsapp\", \"privaci\", \"locat\", \"total\", \"case\", \"coronaviru\", \"covid\", \"coronatrackernep\", \"recoveri\", \"bot\", \"today\", \"sourc\", \"info\", \"avoid\", \"germani\", \"excit\", \"activ\", \"world\", \"usa\", \"onlyfan\", \"read\", \"look\", \"new\", \"cigarett\", \"therew\", \"blow\", \"effort\", \"safeguard\", \"intern\", \"ident\", \"anoth\", \"abandon\", \"big\", \"freedom\", \"telegram\", \"click\", \"provid\", \"instagram\", \"make\", \"platform\", \"becom\", \"say\", \"link\", \"http\"], \"Freq\": [149.0, 38.0, 328.0, 285.0, 39.0, 76.0, 39.0, 63.0, 20.0, 30.0, 14.0, 24.0, 32.0, 9.0, 13.0, 53.0, 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\"info\", \"info\", \"inform\", \"inform\", \"inform\", \"informationsecur\", \"informationsecur\", \"informationsecur\", \"infosec\", \"infosec\", \"infosec\", \"instagram\", \"instal\", \"instal\", \"instal\", \"intern\", \"intern\", \"internet\", \"internet\", \"internet\", \"internet\", \"invest\", \"iotex\", \"iphon\", \"iphon\", \"iphon\", \"iphon\", \"issu\", \"issu\", \"itssssp\", \"januari\", \"januari\", \"join\", \"join\", \"justic\", \"justic\", \"know\", \"know\", \"late\", \"late\", \"laugh\", \"laugh\", \"laugh\", \"launch\", \"law\", \"law\", \"learn\", \"learn\", \"learn\", \"leav\", \"leav\", \"leav\", \"leav\", \"light\", \"like\", \"like\", \"link\", \"link\", \"link\", \"locat\", \"locat\", \"locat\", \"locat\", \"look\", \"look\", \"look\", \"machinelearn\", \"mad\", \"maintain\", \"major\", \"major\", \"make\", \"make\", \"malwar\", \"malwar\", \"man\", \"manag\", \"manag\", \"mandatori\", \"mani\", \"mani\", \"mean\", \"mean\", \"mentalhealth\", \"mentalhealth\", \"messag\", \"messag\", \"million\", \"million\", \"million\", \"mind\", \"mind\", \"mobil\", \"mobil\", \"model\", \"model\", \"model\", \"movi\", \"nca\", \"new\", \"new\", \"new\", \"news\", \"news\", \"number\", \"offici\", \"offici\", \"onlin\", \"onlyfan\", \"parti\", \"password\", \"password\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"phish\", \"phone\", \"phone\", \"platform\", \"platform\", \"platform\", \"plea\", \"plea\", \"plea\", \"polici\", \"polici\", \"polici\", \"post\", \"post\", \"power\", \"power\", \"present\", \"price\", \"price\", \"price\", \"privaci\", \"privaci\", \"privaci\", \"privaci\", \"privaci\", \"privaci\", \"privaci\", \"privaci\", \"privat\", \"privat\", \"problem\", \"problem\", \"problem\", \"project\", \"propos\", \"protect\", \"protect\", \"protect\", \"protocol\", \"protocol\", \"provid\", \"provid\", \"public\", \"public\", \"publicnak\", \"publicsex\", \"publish\", \"publish\", \"rais\", \"rais\", \"rais\", \"ransomwar\", \"ransomwar\", \"ratan\", \"reach\", \"reach\", \"read\", \"read\", \"read\", \"readi\", \"recent\", \"recent\", \"recent\", \"recoveri\", \"recoveri\", \"regul\", \"regul\", \"remov\", \"report\", \"report\", \"report\", \"respect\", \"respect\", \"review\", \"review\", \"right\", \"right\", \"right\", \"risk\", \"risk\", \"riskmanag\", \"riskmanag\", \"riskmanag\", \"rule\", \"rule\", \"rule\", \"safeguard\", \"safeguard\", \"safeti\", \"safeti\", \"say\", \"say\", \"say\", \"secur\", \"secur\", \"secur\", \"secur\", \"sell\", \"sell\", \"sell\", \"sell\", \"servic\", \"servic\", \"servic\", \"set\", \"share\", \"share\", \"share\", \"signal\", \"signalapp\", \"singapor\", \"social\", \"socialmedia\", \"solut\", \"solut\", \"someon\", \"someon\", \"soon\", \"sourc\", \"sourc\", \"sourc\", \"speech\", \"speech\", \"startup\", \"startup\", \"stop\", \"stop\", \"stop\", \"surveil\", \"talk\", \"team\", \"team\", \"tech\", \"tech\", \"tech\", \"technolog\", \"technolog\", \"telegram\", \"telegram\", \"telegram\", \"telegram\", \"telegram\", \"term\", \"thehackersnew\", \"therew\", \"think\", \"think\", \"threat\", \"tiktok\", \"tiktok\", \"tiktok\", \"time\", \"today\", \"today\", \"today\", \"today\", \"topic\", \"topic\", \"total\", \"track\", \"track\", \"track\", \"track\", \"trade\", \"trade\", \"trade\", \"transpar\", \"transpar\", \"trust\", \"trust\", \"trust\", \"turn\", \"tweet\", \"updat\", \"updat\", \"usa\", \"usa\", \"usa\", \"use\", \"use\", \"user\", \"user\", \"user\", \"user\", \"user\", \"violat\", \"violat\", \"violat\", \"violenc\", \"watch\", \"watch\", \"watch\", \"web\", \"web\", \"websit\", \"week\", \"week\", \"whatsapp\", \"whatsapp\", \"whatsapp\", \"whatsapp\", \"whatsappnewpolici\", \"whatsappprivaci\", \"whatsappprivaci\", \"whatsappprivacypolici\", \"wish\", \"wish\", \"work\", \"work\", \"world\", \"world\", \"world\", \"write\", \"write\", \"write\", \"xhv\", \"xtz\", \"xtz\", \"year\", \"year\", \"year\", \"youtub\", \"youtub\", \"ysign\", \"zec\", \"zuckerberg\", \"zuckerberg\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [6, 4, 3, 5, 8, 10, 2, 1, 9, 7]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
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              "}\n",
              "\n",
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              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el156140239332870480252670572\", ldavis_el156140239332870480252670572_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el156140239332870480252670572\", ldavis_el156140239332870480252670572_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el156140239332870480252670572\", ldavis_el156140239332870480252670572_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "PreparedData(topic_coordinates=               x          y  topics  cluster       Freq\n",
              "topic                                                  \n",
              "5      32.420952   8.115230       1        1  30.745612\n",
              "3       0.648955  71.707100       2        1  23.851115\n",
              "2      71.019684   9.971006       3        1  13.114678\n",
              "4       3.785981  33.185135       4        1   6.961831\n",
              "7     -42.666500  -9.222070       5        1   5.739108\n",
              "9      -3.621689 -43.526516       6        1   5.700687\n",
              "1      -3.632402  -4.171708       7        1   3.976993\n",
              "0     -36.584400  35.804413       8        1   3.931314\n",
              "8      44.391380  50.615383       9        1   3.517726\n",
              "6      40.235809 -31.591665      10        1   2.460936, topic_info=         Term        Freq       Total Category  logprob  loglift\n",
              "358  telegram  149.000000  149.000000  Default  30.0000  30.0000\n",
              "20        amp   38.000000   38.000000  Default  29.0000  29.0000\n",
              "183      http  328.000000  328.000000  Default  28.0000  28.0000\n",
              "286   privaci  285.000000  285.000000  Default  27.0000  27.0000\n",
              "278    polici   39.000000   39.000000  Default  26.0000  26.0000\n",
              "..        ...         ...         ...      ...      ...      ...\n",
              "275  platform    0.148675   10.542272  Topic10  -6.4525  -0.5568\n",
              "31      becom    0.155334   14.685255  Topic10  -6.4087  -0.8444\n",
              "325       say    0.142504    9.532357  Topic10  -6.4949  -0.4985\n",
              "225      link    0.127904    6.790135  Topic10  -6.6030  -0.2673\n",
              "183      http    0.179234  328.171358  Topic10  -6.2656  -3.8080\n",
              "\n",
              "[420 rows x 6 columns], token_table=      Topic      Freq        Term\n",
              "term                             \n",
              "0         2  0.721221     abandon\n",
              "1         4  0.173180      accept\n",
              "1         6  0.519540      accept\n",
              "1         9  0.173180      accept\n",
              "3         1  0.350846     account\n",
              "...     ...       ...         ...\n",
              "409       3  0.650153      youtub\n",
              "410       2  0.577655       ysign\n",
              "411       1  0.697200         zec\n",
              "412       2  0.331651  zuckerberg\n",
              "412       7  0.331651  zuckerberg\n",
              "\n",
              "[601 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 4, 3, 5, 8, 10, 2, 1, 9, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCfGU128hK2A",
        "outputId": "ad9907ee-1206-4fa1-ee68-b1039ac8c0e5"
      },
      "source": [
        "for i,topic in enumerate(lda_model.components_):\n",
        "  print(f'Top 10 words for topic #{i}:')\n",
        "  print([vectorizer.get_feature_names()[i] for i in topic.argsort()[-10:]])\n",
        "  print('\\n')"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top 10 words for topic #0:\n",
            "['fuw', 'readi', 'everi', 'locat', 'download', 'day', 'good', 'post', 'telegram', 'http']\n",
            "\n",
            "\n",
            "Top 10 words for topic #1:\n",
            "['becom', 'review', 'everyon', 'hear', 'nca', 'corrupt', 'encrochat', 'privaci', 'justic', 'amp']\n",
            "\n",
            "\n",
            "Top 10 words for topic #2:\n",
            "['regul', 'startup', 'threat', 'manag', 'complianc', 'gdpr', 'cybersecur', 'secur', 'privaci', 'http']\n",
            "\n",
            "\n",
            "Top 10 words for topic #3:\n",
            "['help', 'whatsapp', 'commun', 'right', 'infosec', 'new', 'secur', 'cybersecur', 'http', 'privaci']\n",
            "\n",
            "\n",
            "Top 10 words for topic #4:\n",
            "['servic', 'whatsapp', 'app', 'internet', 'agre', 'facebook', 'updat', 'term', 'polici', 'privaci']\n",
            "\n",
            "\n",
            "Top 10 words for topic #5:\n",
            "['app', 'late', 'signal', 'join', 'whatsappnewpolici', 'use', 'whatsapp', 'privaci', 'telegram', 'http']\n",
            "\n",
            "\n",
            "Top 10 words for topic #6:\n",
            "['telegram', 'sourc', 'coronatrackernep', 'bot', 'coronaviru', 'today', 'recoveri', 'covid', 'case', 'total']\n",
            "\n",
            "\n",
            "Top 10 words for topic #7:\n",
            "['datum', 'ransomwar', 'dataprotect', 'phish', 'malwar', 'privaci', 'cybersecur', 'infosec', 'databreach', 'http']\n",
            "\n",
            "\n",
            "Top 10 words for topic #8:\n",
            "['delet', 'polici', 'alreadi', 'download', 'chang', 'whatsapp', 'movi', 'peopl', 'instal', 'telegram']\n",
            "\n",
            "\n",
            "Top 10 words for topic #9:\n",
            "['bigcoinvietnam', 'team', 'cloudcomput', 'reach', 'dataprotect', 'host', 'privaci', 'http', 'cloud', 'infosec']\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mH19QQK0hR7z"
      },
      "source": [
        "topic_values = lda_model.transform(data_matrix)\n",
        "df['Topic'] = topic_values.argmax(axis=1)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "auP4y6IOhr_B",
        "outputId": "28c06856-ac7c-46e4-935b-1b2494084881"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweets_cleaned</th>\n",
              "      <th>Topic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>telegram paid servic registr open run calendar...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>corona info germani new case today recoveri to...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>check icon telegram bot pleas look step step g...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rt africadem launch alert join new scholarship...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>rt hannaliubakova belaru day belarusian came s...</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      tweets_cleaned  Topic\n",
              "0  telegram paid servic registr open run calendar...      1\n",
              "1  corona info germani new case today recoveri to...      4\n",
              "2  check icon telegram bot pleas look step step g...      9\n",
              "3  rt africadem launch alert join new scholarship...      6\n",
              "4  rt hannaliubakova belaru day belarusian came s...      6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_KMx8l5paOKt"
      },
      "source": [
        "# VADER Sentiment Analyzer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H9BmsaDaNe0",
        "outputId": "bdb9223a-02bb-49c8-bf66-e879135c8bf9"
      },
      "source": [
        "sid = SentimentIntensityAnalyzer()\n",
        "new_df = df['tweets_cleaned'].apply(lambda x: sid.polarity_scores(x))\n",
        "new_df.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    {'neg': 0.0, 'neu': 0.775, 'pos': 0.225, 'comp...\n",
              "1    {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...\n",
              "2    {'neg': 0.0, 'neu': 0.915, 'pos': 0.085, 'comp...\n",
              "3    {'neg': 0.0, 'neu': 0.8, 'pos': 0.2, 'compound...\n",
              "4    {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...\n",
              "Name: tweets_cleaned, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3LSDbQg8auUD",
        "outputId": "506470e2-49e4-4067-f771-6e8efa3909ff"
      },
      "source": [
        "compound_sentiment = []\n",
        "for i in range(0,len(new_df)):\n",
        "    compound_sentiment.append(new_df.loc[i]['compound'])\n",
        "print(compound_sentiment)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "C-yQM5fyayV0",
        "outputId": "11b93321-50e2-4725-b518-d628934d2433"
      },
      "source": [
        "df['compound_sentiment'] = pd.DataFrame(compound_sentiment)\n",
        "df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweets_cleaned</th>\n",
              "      <th>Topic</th>\n",
              "      <th>compound_sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pleas help retweet dj bash open nice clubhous ...</td>\n",
              "      <td>9</td>\n",
              "      <td>0.8126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>use watusi io fouadraheb add new featur whatsa...</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>excit announc strateg partnership dreadbong dr...</td>\n",
              "      <td>2</td>\n",
              "      <td>0.4019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rt sarcastiqlonda user privaci data whatsapp l...</td>\n",
              "      <td>4</td>\n",
              "      <td>0.3612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>whatsappnewpolici whatsapp whatsappprivacypoli...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                      tweets_cleaned  Topic  compound_sentiment\n",
              "0  pleas help retweet dj bash open nice clubhous ...      9              0.8126\n",
              "1  use watusi io fouadraheb add new featur whatsa...      8              0.0000\n",
              "2  excit announc strateg partnership dreadbong dr...      2              0.4019\n",
              "3  rt sarcastiqlonda user privaci data whatsapp l...      4              0.3612\n",
              "4  whatsappnewpolici whatsapp whatsappprivacypoli...      0              0.0000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dR_bM4JhEqu_"
      },
      "source": [
        "## Important:\n",
        "\n",
        "Choose the python code below that match the name of the file that you uploaded above with _sentiment behind the name. For example, if you uploaded the telegram_2020.csv file, please use the df.to_csv('telegram_2020_sentiment.csv') code and ignore the rest. This code will export the dataframe with sentiment polarity score to a csv file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ljQUFsF0bT4B",
        "outputId": "3515c4d1-e288-4b58-b36d-cb1e8afb93ef"
      },
      "source": [
        "df.to_csv('telegram_2020_sentiment.csv')\n",
        "files.download('telegram_2020_sentiment.csv')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_c5843a30-2041-4ef1-9cbd-ebea06bee457\", \"telegram_2020_sentiment.csv\", 48948)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Vqci3CgMVciR",
        "outputId": "3053792a-c944-4e81-dfca-97eba8e988ee"
      },
      "source": [
        "df.to_csv('telegram_2021_sentiment.csv')\n",
        "files.download('telegram_2021_sentiment.csv')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_96d9d2ff-47df-4103-a550-15f9178b8a24\", \"telegram_2021_sentiment.csv\", 48948)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "k0ghbhSwVvsW",
        "outputId": "48f76cc8-fecb-485f-c89e-611171035aec"
      },
      "source": [
        "df.to_csv('whatsapp_2020_sentiment.csv')\n",
        "files.download('whatsapp_2020_sentiment.csv')"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_656e7fa5-eea8-448d-8d22-3005ccaf1b69\", \"whatsapp_2020_sentiment.csv\", 44684)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "x6s1qhS8Vqs_",
        "outputId": "3eef7d92-ac32-4c2f-f3a2-4acb958e6e7e"
      },
      "source": [
        "df.to_csv('whatsapp_2021_sentiment.csv')\n",
        "files.download('whatsapp_2021_sentiment.csv')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_b8c7b3a5-d0a8-43b5-b01c-c2f16411b9f7\", \"whatsapp_2021_sentiment.csv\", 45591)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ebIz0sDAV0p7",
        "outputId": "7e2c52ff-c3b8-4706-c09d-af5321c08727"
      },
      "source": [
        "df.to_csv('whatsapp_feature_sentiment.csv')\n",
        "files.download('whatsapp_feature_sentiment.csv')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_2f881560-cd5d-41d1-bf97-259490dbd271\", \"whatsapp_feature_sentiment.csv\", 46165)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "moCOe_q_V6G9",
        "outputId": "0184de66-0cc4-4016-f3a1-9631fc88e5b1"
      },
      "source": [
        "df.to_csv('whatsapp_privacy_sentiment.csv')\n",
        "files.download('whatsapp_privacy_sentiment.csv')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_4d5df9ed-c4d1-4adc-ba1b-4255cba1a1e3\", \"whatsapp_privacy_sentiment.csv\", 49506)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "TBNGD0seV9vT",
        "outputId": "ab801851-a084-4df2-dc3a-7f4c91285d39"
      },
      "source": [
        "df.to_csv('telegram_feature_sentiment.csv')\n",
        "files.download('telegram_feature_sentiment.csv')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_00b62dda-eb6b-4d1c-bcad-c16b2bae5b28\", \"telegram_feature_sentiment.csv\", 48010)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FGu0N1YyV-CZ",
        "outputId": "dd3aed84-ba17-4bb3-9e13-211485a181f5"
      },
      "source": [
        "df.to_csv('telegram_privacy_sentiment.csv')\n",
        "files.download('telegram_privacy_sentiment.csv')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e3a7316e-6218-4a7e-b0e9-577fc770a6b5\", \"telegram_privacy_sentiment.csv\", 54994)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}