LDA_model_with_topic_coherence_on_20_news_group.ipynb

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    "colab": {
      "name": " LDA model with topic coherence on 20 news group.ipynb",
      "provenance": [],
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    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries."
      ],
      "metadata": {
        "id": "thxXIT1HOSC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pyldavis"
      ],
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        },
        "id": "og8yCw_mMt3I",
        "outputId": "980e1889-f106-4bbd-f240-4e7af9138e61"
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            "Requirement already satisfied: pyldavis in /usr/local/lib/python3.7/dist-packages (3.3.1)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from pyldavis) (1.3.5)\n",
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from pyldavis) (0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pyldavis) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.7/dist-packages (from pyldavis) (1.21.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pyldavis) (1.4.1)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyldavis) (2.8.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pyldavis) (1.0.2)\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from pyldavis) (3.6.0)\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "HDsTvgdrRV_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "execution_count": null,
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        "id": "-7PTuFr0L0IY",
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          "metadata": {},
          "execution_count": 4
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      ],
      "source": [
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pprint import pprint\n",
        "\n",
        "# Gensim\n",
        "import gensim\n",
        "import gensim.corpora as corpora\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.models import CoherenceModel\n",
        "\n",
        "# spacy for lemmatization\n",
        "import spacy\n",
        "\n",
        "# Plotting tools\n",
        "import pyLDAvis\n",
        "#import pyLDAvis.gensim  # don't skip this\n",
        "import pyLDAvis.gensim_models as gensimvis\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Enable logging for gensim - optional\n",
        "import logging\n",
        "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n",
        "\n",
        "# Run in python console\n",
        "import nltk; nltk.download('stopwords')\n",
        "\n",
        "# Run in terminal or command prompt\n",
        "# python3 -m spacy download en"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# NLTK Stop words\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')\n",
        "stop_words.extend(['from', 'subject', 're', 'edu', 'use'])"
      ],
      "metadata": {
        "id": "dms4GRB7NKF2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Dataset"
      ],
      "metadata": {
        "id": "ixWjDyQZOaJS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "dataa= datasets.fetch_20newsgroups()"
      ],
      "metadata": {
        "id": "qqaJYbrbNzIW"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "dataa['Target'] = pd.Series(data=dataa)"
      ],
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        "id": "tAJdaV2CP31U"
      },
      "execution_count": null,
      "outputs": []
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        "# put your data into a dataframe\n",
        "news_df = pd.DataFrame({'News': dataa.data,\n",
        "                       'Target': dataa.target})\n",
        "\n",
        "# get dimensions of data \n",
        "news_df.shape"
      ],
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              "2      From: twillis@ec.ecn.purdue.edu (Thomas E Will...       4\n",
              "3      From: jgreen@amber (Joe Green)\\nSubject: Re: W...       1\n",
              "4      From: jcm@head-cfa.harvard.edu (Jonathan McDow...      14\n",
              "...                                                  ...     ...\n",
              "11309  From: jim.zisfein@factory.com (Jim Zisfein) \\n...      13\n",
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              "11311  From: westes@netcom.com (Will Estes)\\nSubject:...       3\n",
              "11312  From: steve@hcrlgw (Steven Collins)\\nSubject: ...       1\n",
              "11313  From: gunning@cco.caltech.edu (Kevin J. Gunnin...       8\n",
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        "# replace target names from target numbers in our news data frame\n",
        "news_df['Target_name'] = news_df['Target'].apply(lambda x: dataa.target_names[x])"
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              "0  From: lerxst@wam.umd.edu (where's my thing)\\nS...       7   \n",
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              "3  From: jgreen@amber (Joe Green)\\nSubject: Re: W...       1   \n",
              "4  From: jcm@head-cfa.harvard.edu (Jonathan McDow...      14   \n",
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              "             Target_name  \n",
              "0              rec.autos  \n",
              "1  comp.sys.mac.hardware  \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f106ac0c-fcc7-4c91-9a48-3b6d15103edd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spitting 20Newsgroup in 6 Groups and viewing"
      ],
      "metadata": {
        "id": "J5ZFlEi53kYH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plot distribution of topics in news data\n",
        "fig = plt.figure(figsize=[10,7])\n",
        "ax = sns.countplot(news_df['Target_name'], color=sns.xkcd_rgb['greenish cyan'])\n",
        "plt.title('Distribution of Topics')\n",
        "plt.xlabel('Topics')\n",
        "plt.ylabel('Count of topics')\n",
        "plt.xticks(rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "DeSqFNcRRFMB",
        "outputId": "363633ed-657c-4986-e87e-16c658498992"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19]), <a list of 20 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_df['Target_name']=news_df['Target_name'].replace(['alt.atheism',\n",
        " 'comp.graphics',\n",
        " 'comp.os.ms-windows.misc',\n",
        " 'comp.sys.ibm.pc.hardware',\n",
        " 'comp.sys.mac.hardware',\n",
        " 'comp.windows.x',\n",
        " 'misc.forsale',\n",
        " 'rec.autos',\n",
        " 'rec.motorcycles',\n",
        " 'rec.sport.baseball',\n",
        " 'rec.sport.hockey',\n",
        " 'sci.crypt',\n",
        " 'sci.electronics',\n",
        " 'sci.med',\n",
        " 'sci.space',\n",
        " 'soc.religion.christian',\n",
        " 'talk.politics.guns',\n",
        " 'talk.politics.mideast',\n",
        " 'talk.politics.misc',\n",
        " 'talk.religion.misc'],['Religion','Tech','Tech','Tech','Tech','Tech','Auto','Auto','Auto','sport','sport','Science','Science','Science','Science','Religion','Politics','Politics','Politics','Religion'])"
      ],
      "metadata": {
        "id": "3jev9guvRecE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot distribution of topics in news data\n",
        "fig = plt.figure(figsize=[10,7])\n",
        "ax = sns.countplot(news_df['Target_name'], color=sns.xkcd_rgb['orange'])\n",
        "plt.title('Distribution of Topics')\n",
        "plt.xlabel('Topics')\n",
        "plt.ylabel('Count of topics')\n",
        "plt.xticks(rotation=90)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "TgcDOytbRrXB",
        "outputId": "54cfecfd-88f8-400d-c968-e9d8483e47c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5]), <a list of 6 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## NLP Preprocessing"
      ],
      "metadata": {
        "id": "GWJZ-1-j6imp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "news_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "m1fZ4MX4Txva",
        "outputId": "7fead8ae-d40d-42a6-a268-d83e5b5fadba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    News  Target Target_name\n",
              "0      From: lerxst@wam.umd.edu (where's my thing)\\nS...       7        Auto\n",
              "1      From: guykuo@carson.u.washington.edu (Guy Kuo)...       4        Tech\n",
              "2      From: twillis@ec.ecn.purdue.edu (Thomas E Will...       4        Tech\n",
              "3      From: jgreen@amber (Joe Green)\\nSubject: Re: W...       1        Tech\n",
              "4      From: jcm@head-cfa.harvard.edu (Jonathan McDow...      14     Science\n",
              "...                                                  ...     ...         ...\n",
              "11309  From: jim.zisfein@factory.com (Jim Zisfein) \\n...      13     Science\n",
              "11310  From: ebodin@pearl.tufts.edu\\nSubject: Screen ...       4        Tech\n",
              "11311  From: westes@netcom.com (Will Estes)\\nSubject:...       3        Tech\n",
              "11312  From: steve@hcrlgw (Steven Collins)\\nSubject: ...       1        Tech\n",
              "11313  From: gunning@cco.caltech.edu (Kevin J. Gunnin...       8        Auto\n",
              "\n",
              "[11314 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a792f086-08a4-4fc2-8418-48a78a458352\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>News</th>\n",
              "      <th>Target</th>\n",
              "      <th>Target_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>From: lerxst@wam.umd.edu (where's my thing)\\nS...</td>\n",
              "      <td>7</td>\n",
              "      <td>Auto</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>From: guykuo@carson.u.washington.edu (Guy Kuo)...</td>\n",
              "      <td>4</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>From: twillis@ec.ecn.purdue.edu (Thomas E Will...</td>\n",
              "      <td>4</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>From: jgreen@amber (Joe Green)\\nSubject: Re: W...</td>\n",
              "      <td>1</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>From: jcm@head-cfa.harvard.edu (Jonathan McDow...</td>\n",
              "      <td>14</td>\n",
              "      <td>Science</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11309</th>\n",
              "      <td>From: jim.zisfein@factory.com (Jim Zisfein) \\n...</td>\n",
              "      <td>13</td>\n",
              "      <td>Science</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11310</th>\n",
              "      <td>From: ebodin@pearl.tufts.edu\\nSubject: Screen ...</td>\n",
              "      <td>4</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11311</th>\n",
              "      <td>From: westes@netcom.com (Will Estes)\\nSubject:...</td>\n",
              "      <td>3</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11312</th>\n",
              "      <td>From: steve@hcrlgw (Steven Collins)\\nSubject: ...</td>\n",
              "      <td>1</td>\n",
              "      <td>Tech</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11313</th>\n",
              "      <td>From: gunning@cco.caltech.edu (Kevin J. Gunnin...</td>\n",
              "      <td>8</td>\n",
              "      <td>Auto</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11314 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a792f086-08a4-4fc2-8418-48a78a458352')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a792f086-08a4-4fc2-8418-48a78a458352');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to list\n",
        "data = news_df.News.values.tolist()\n",
        "\n",
        "# Remove Emails\n",
        "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]\n",
        "\n",
        "# Remove new line characters\n",
        "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
        "\n",
        "# Remove distracting single quotes\n",
        "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
        "\n",
        "pprint(data[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5_7FXcuR3jN",
        "outputId": "8bfdc38d-d30d-4f25-a97c-3bfd89ad861f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['From: (wheres my thing) Subject: WHAT car is this!? Nntp-Posting-Host: '\n",
            " 'rac3.wam.umd.edu Organization: University of Maryland, College Park Lines: '\n",
            " '15 I was wondering if anyone out there could enlighten me on this car I saw '\n",
            " 'the other day. It was a 2-door sports car, looked to be from the late 60s/ '\n",
            " 'early 70s. It was called a Bricklin. The doors were really small. In '\n",
            " 'addition, the front bumper was separate from the rest of the body. This is '\n",
            " 'all I know. If anyone can tellme a model name, engine specs, years of '\n",
            " 'production, where this car is made, history, or whatever info you have on '\n",
            " 'this funky looking car, please e-mail. Thanks, - IL ---- brought to you by '\n",
            " 'your neighborhood Lerxst ---- ']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sent_to_words(sentences):\n",
        "    for sentence in sentences:\n",
        "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
        "\n",
        "data_words = list(sent_to_words(data))\n",
        "\n",
        "print(data_words[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_aOBD9E-UBzs",
        "outputId": "8f7c071c-3662-47f0-86f7-48bf29f38677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp', 'posting', 'host', 'rac', 'wam', 'umd', 'edu', 'organization', 'university', 'of', 'maryland', 'college', 'park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front', 'bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build the bigram and trigram models\n",
        "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
        "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
        "\n",
        "# Faster way to get a sentence clubbed as a trigram/bigram\n",
        "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
        "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
        "\n",
        "# See trigram example\n",
        "print(trigram_mod[bigram_mod[data_words[0]]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgrt9JyNUSL_",
        "outputId": "4c9d79f5-d82a-4c65-94b1-17973ff606ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/phrases.py:598: UserWarning: For a faster implementation, use the gensim.models.phrases.Phraser class\n",
            "  warnings.warn(\"For a faster implementation, use the gensim.models.phrases.Phraser class\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['from', 'wheres', 'my', 'thing', 'subject', 'what', 'car', 'is', 'this', 'nntp_posting_host', 'rac_wam_umd_edu', 'organization', 'university', 'of', 'maryland_college_park', 'lines', 'was', 'wondering', 'if', 'anyone', 'out', 'there', 'could', 'enlighten', 'me', 'on', 'this', 'car', 'saw', 'the', 'other', 'day', 'it', 'was', 'door', 'sports', 'car', 'looked', 'to', 'be', 'from', 'the', 'late', 'early', 'it', 'was', 'called', 'bricklin', 'the', 'doors', 'were', 'really', 'small', 'in', 'addition', 'the', 'front_bumper', 'was', 'separate', 'from', 'the', 'rest', 'of', 'the', 'body', 'this', 'is', 'all', 'know', 'if', 'anyone', 'can', 'tellme', 'model', 'name', 'engine', 'specs', 'years', 'of', 'production', 'where', 'this', 'car', 'is', 'made', 'history', 'or', 'whatever', 'info', 'you', 'have', 'on', 'this', 'funky', 'looking', 'car', 'please', 'mail', 'thanks', 'il', 'brought', 'to', 'you', 'by', 'your', 'neighborhood', 'lerxst']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
        "def remove_stopwords(texts):\n",
        "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
        "\n",
        "def make_bigrams(texts):\n",
        "    return [bigram_mod[doc] for doc in texts]\n",
        "\n",
        "def make_trigrams(texts):\n",
        "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
        "\n",
        "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
        "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
        "    texts_out = []\n",
        "    for sent in texts:\n",
        "        doc = nlp(\" \".join(sent)) \n",
        "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
        "    return texts_out"
      ],
      "metadata": {
        "id": "QcWT4J9yUgTf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Stop Words\n",
        "data_words_nostops = remove_stopwords(data_words)\n",
        "\n",
        "# Form Bigrams\n",
        "data_words_bigrams = make_bigrams(data_words_nostops)\n",
        "\n",
        "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
        "# python3 -m spacy download en\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
        "\n",
        "# Do lemmatization keeping only noun, adj, vb, adv\n",
        "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
        "\n",
        "print(data_lemmatized[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVQ1Ix2kUrt9",
        "outputId": "3dab5bcd-3db1-4531-c32e-e3bcb4b225ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['where', 'thing', 'car', 'nntp_poste', 'host', 'park', 'line', 'wonder', 'could', 'enlighten', 'car', 'see', 'day', 'door', 'sport', 'car', 'look', 'late', 'early', 'call', 'bricklin', 'door', 'really', 'small', 'addition', 'separate', 'rest', 'body', 'know', 'tellme', 'model', 'name', 'engine', 'year', 'production', 'car', 'make', 'history', 'info', 'funky', 'look', 'car', 'mail', 'thank', 'bring', 'neighborhood', 'lerxst']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Dictionary\n",
        "id2word = corpora.Dictionary(data_lemmatized)\n",
        "\n",
        "# Create Corpus\n",
        "texts = data_lemmatized\n",
        "\n",
        "# Term Document Frequency\n",
        "corpus = [id2word.doc2bow(text) for text in texts]\n",
        "\n",
        "# View\n",
        "print(corpus[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9zLla83U02H",
        "outputId": "9b00dde1-6fad-4d0f-9d58-7ac995c983f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 5), (6, 1), (7, 1), (8, 2), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (18, 1), (19, 1), (20, 2), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 1), (27, 1), (28, 1), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 1), (36, 1), (37, 1), (38, 1), (39, 1), (40, 1)]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2word[0]\n",
        "# here '0' is the word id of the addition."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Qj8D8pkXWEAU",
        "outputId": "a9427687-1ddb-4c76-f079-646719bb8e45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'addition'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2word[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "eJvQq6YlFVWW",
        "outputId": "6f673373-4b82-4374-ae2e-f9f075d0e744"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'body'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Human readable format of corpus (term-frequency)\n",
        "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2OkSQKfWJgC",
        "outputId": "01831f26-ea55-487b-eaac-d8eb9058bfef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[('addition', 1),\n",
              "  ('body', 1),\n",
              "  ('bricklin', 1),\n",
              "  ('bring', 1),\n",
              "  ('call', 1),\n",
              "  ('car', 5),\n",
              "  ('could', 1),\n",
              "  ('day', 1),\n",
              "  ('door', 2),\n",
              "  ('early', 1),\n",
              "  ('engine', 1),\n",
              "  ('enlighten', 1),\n",
              "  ('funky', 1),\n",
              "  ('history', 1),\n",
              "  ('host', 1),\n",
              "  ('info', 1),\n",
              "  ('know', 1),\n",
              "  ('late', 1),\n",
              "  ('lerxst', 1),\n",
              "  ('line', 1),\n",
              "  ('look', 2),\n",
              "  ('mail', 1),\n",
              "  ('make', 1),\n",
              "  ('model', 1),\n",
              "  ('name', 1),\n",
              "  ('neighborhood', 1),\n",
              "  ('nntp_poste', 1),\n",
              "  ('park', 1),\n",
              "  ('production', 1),\n",
              "  ('really', 1),\n",
              "  ('rest', 1),\n",
              "  ('see', 1),\n",
              "  ('separate', 1),\n",
              "  ('small', 1),\n",
              "  ('sport', 1),\n",
              "  ('tellme', 1),\n",
              "  ('thank', 1),\n",
              "  ('thing', 1),\n",
              "  ('where', 1),\n",
              "  ('wonder', 1),\n",
              "  ('year', 1)]]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building LDA model with 6 Topics"
      ],
      "metadata": {
        "id": "CkTYrMsuI0SE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build LDA model\n",
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
        "                                           id2word=id2word,\n",
        "                                           num_topics=6, \n",
        "                                           random_state=100,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=10,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)"
      ],
      "metadata": {
        "id": "F2giY6UEWNo8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the Keyword in the 10 topics\n",
        "pprint(lda_model.print_topics())\n",
        "doc_lda = lda_model[corpus]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN3OplhWXzZv",
        "outputId": "5b3f1c8c-8a63-472f-cd96-9b2a08698c2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0,\n",
            "  '0.020*\"go\" + 0.013*\"would\" + 0.011*\"year\" + 0.011*\"get\" + 0.011*\"write\" + '\n",
            "  '0.011*\"say\" + 0.010*\"line\" + 0.010*\"think\" + 0.010*\"be\" + 0.010*\"time\"'),\n",
            " (1,\n",
            "  '0.013*\"people\" + 0.013*\"say\" + 0.013*\"would\" + 0.011*\"write\" + 0.010*\"may\" '\n",
            "  '+ 0.009*\"believe\" + 0.008*\"think\" + 0.008*\"reason\" + 0.008*\"make\" + '\n",
            "  '0.008*\"know\"'),\n",
            " (2,\n",
            "  '0.012*\"program\" + 0.011*\"file\" + 0.009*\"use\" + 0.009*\"window\" + '\n",
            "  '0.008*\"include\" + 0.007*\"available\" + 0.007*\"run\" + 0.007*\"system\" + '\n",
            "  '0.007*\"software\" + 0.007*\"also\"'),\n",
            " (3,\n",
            "  '0.032*\"line\" + 0.019*\"would\" + 0.016*\"write\" + 0.012*\"host\" + '\n",
            "  '0.010*\"article\" + 0.010*\"drive\" + 0.010*\"get\" + 0.010*\"use\" + 0.009*\"thank\" '\n",
            "  '+ 0.009*\"be\"'),\n",
            " (4,\n",
            "  '0.047*\"ax\" + 0.040*\"max\" + 0.020*\"patient\" + 0.012*\"space\" + 0.010*\"launch\" '\n",
            "  '+ 0.009*\"di_di\" + 0.009*\"orbit\" + 0.008*\"disease\" + 0.008*\"tumor\" + '\n",
            "  '0.008*\"satellite\"'),\n",
            " (5,\n",
            "  '0.011*\"government\" + 0.009*\"people\" + 0.008*\"public\" + 0.008*\"state\" + '\n",
            "  '0.007*\"gun\" + 0.006*\"kill\" + 0.005*\"attack\" + 0.004*\"right\" + 0.004*\"year\" '\n",
            "  '+ 0.004*\"issue\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Measuring topic coherence\n",
        "### Topic Coherence Metric assesses is how well a topic is ‘supported’ by a text set (called reference corpus)"
      ],
      "metadata": {
        "id": "VsQ2dp-uJbv4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute Perplexity\n",
        "print('\\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.\n",
        "\n",
        "# Compute Coherence Score\n",
        "coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
        "coherence_lda = coherence_model_lda.get_coherence()\n",
        "print('\\nCoherence Score: ', coherence_lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dR6do-fbX4-y",
        "outputId": "239b7dae-8027-4242-ffb2-41f0f8fbdca4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Perplexity:  -8.116451825227598\n",
            "\n",
            "Coherence Score:  0.4372555642195248\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyLDAvis\n",
        "import pyLDAvis.gensim_models as gensimvis\n",
        "pyLDAvis.enable_notebook()\n",
        "\n",
        "# feed the LDA model into the pyLDAvis instance\n",
        "lda_viz = gensimvis.prepare(lda_model, corpus, id2word)\n",
        "lda_viz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "uFhrhSOIZEhc",
        "outputId": "e040d55d-f847-42e1-f52c-d0b29665af3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  by='saliency', ascending=False).head(R).drop('saliency', 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "1     -0.146281 -0.033487       1        1  24.540632\n",
              "0     -0.160427 -0.144503       2        1  22.328179\n",
              "3     -0.162292 -0.084302       3        1  21.229432\n",
              "2      0.035535  0.257748       4        1  15.437675\n",
              "5      0.049628  0.157583       5        1  13.717003\n",
              "4      0.383837 -0.153039       6        1   2.747078, topic_info=          Term          Freq         Total Category  logprob  loglift\n",
              "19        line  11664.000000  11664.000000  Default  30.0000  30.0000\n",
              "113         go   5638.000000   5638.000000  Default  29.0000  29.0000\n",
              "3685        ax   1462.000000   1462.000000  Default  28.0000  28.0000\n",
              "1780       max   1238.000000   1238.000000  Default  27.0000  27.0000\n",
              "300        say   6412.000000   6412.000000  Default  26.0000  26.0000\n",
              "...        ...           ...           ...      ...      ...      ...\n",
              "3309     taste    105.741184    146.961776   Topic6  -5.6813   3.2655\n",
              "1227     earth    138.612119    430.498803   Topic6  -5.4106   2.4614\n",
              "3304      pain    120.928990    338.120326   Topic6  -5.5471   2.5664\n",
              "703      study    123.887108    561.087804   Topic6  -5.5229   2.0841\n",
              "686   research    111.303706    577.175486   Topic6  -5.6301   1.9487\n",
              "\n",
              "[381 rows x 6 columns], token_table=      Topic      Freq     Term\n",
              "term                          \n",
              "1211      1  0.927958   accept\n",
              "1211      4  0.071694   accept\n",
              "176       1  0.097940  address\n",
              "176       3  0.023045  address\n",
              "176       4  0.800800  address\n",
              "...     ...       ...      ...\n",
              "197       1  0.310489    write\n",
              "197       2  0.286061    write\n",
              "197       3  0.403380    write\n",
              "40        2  0.808315     year\n",
              "40        5  0.191244     year\n",
              "\n",
              "[624 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 4, 3, 6, 5])"
            ],
            "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_el581401041611074085060759082\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el581401041611074085060759082_data = {\"mdsDat\": {\"x\": [-0.14628095547430964, -0.16042721503849178, -0.1622921741539784, 0.03553517096663423, 0.04962779203937761, 0.38383738166076736], \"y\": [-0.0334869395384468, -0.14450343518307637, -0.0843018580868333, 0.2577475443766135, 0.1575832741336442, -0.15303858570190126], \"topics\": [1, 2, 3, 4, 5, 6], \"cluster\": [1, 1, 1, 1, 1, 1], \"Freq\": [24.540632011399833, 22.32817937448673, 21.229432492054766, 15.437674714193724, 13.717002952790782, 2.747078455074163]}, \"tinfo\": {\"Term\": [\"line\", \"go\", \"ax\", \"max\", \"say\", \"write\", \"people\", \"host\", \"year\", \"program\", \"get\", \"drive\", \"file\", \"government\", \"believe\", \"thank\", \"would\", \"evidence\", \"be\", \"window\", \"use\", \"think\", \"reason\", \"space\", \"key\", \"team\", \"public\", \"know\", \"article\", \"car\", \"evidence\", \"claim\", \"sense\", \"faith\", \"religion\", \"physical\", \"god\", \"argument\", \"truth\", \"atheist\", \"belief\", \"human\", \"church\", \"valid\", \"conclusion\", \"season\", \"christian\", \"religious\", \"existence\", \"indeed\", \"sin\", \"therefore\", \"nature\", \"respect\", \"judge\", \"revelation\", \"moral\", \"argue\", \"ignore\", \"proof\", \"believe\", \"prove\", \"reason\", \"explain\", \"science\", \"word\", \"statement\", \"accept\", \"life\", \"exist\", \"true\", \"matter\", \"people\", \"fact\", \"say\", \"mean\", \"may\", \"agree\", \"man\", \"question\", \"many\", \"think\", \"would\", \"write\", \"make\", \"know\", \"thing\", \"read\", \"article\", \"can\", \"even\", \"point\", \"must\", \"see\", \"way\", \"also\", \"come\", \"line\", \"give\", \"team\", \"game\", \"play\", \"player\", \"hit\", \"fan\", \"hockey\", \"watch\", \"score\", \"hell\", \"lebanese\", \"brain\", \"wing\", \"trade\", \"ride\", \"normal\", \"flame\", \"throw\", \"playoff\", \"eye\", \"thought\", \"pull\", \"tv\", \"kid\", \"average\", \"laugh\", \"atheism\", \"loss\", \"stay\", \"beat\", \"go\", \"lose\", \"chance\", \"win\", \"year\", \"last\", \"guy\", \"back\", \"fire\", \"s\", \"start\", \"happen\", \"remember\", \"day\", \"next\", \"get\", \"take\", \"time\", \"bad\", \"first\", \"think\", \"see\", \"be\", \"come\", \"say\", \"leave\", \"well\", \"good\", \"would\", \"write\", \"article\", \"line\", \"make\", \"know\", \"point\", \"could\", \"drive\", \"car\", \"card\", \"buy\", \"chip\", \"driver\", \"price\", \"com\", \"sale\", \"color\", \"speed\", \"cheap\", \"fast\", \"phone\", \"tape\", \"slow\", \"monitor\", \"replace\", \"clipper\", \"bus\", \"engine\", \"scsi\", \"encrypt\", \"cpu\", \"performance\", \"ripem\", \"box\", \"nntp_posting\", \"scan\", \"controller\", \"seller\", \"thank\", \"sell\", \"key\", \"device\", \"bit\", \"host\", \"line\", \"need\", \"cost\", \"reply\", \"nntp_poste\", \"use\", \"would\", \"write\", \"work\", \"problem\", \"get\", \"want\", \"be\", \"article\", \"look\", \"organization\", \"know\", \"system\", \"new\", \"good\", \"could\", \"make\", \"much\", \"try\", \"file\", \"window\", \"software\", \"image\", \"version\", \"entry\", \"application\", \"screen\", \"bike\", \"disk\", \"pin\", \"display\", \"server\", \"graphic\", \"package\", \"video\", \"input\", \"item\", \"connect\", \"format\", \"internal\", \"telnet\", \"mount\", \"motif\", \"library\", \"external\", \"install\", \"mouse\", \"manual\", \"select\", \"available\", \"program\", \"user\", \"faq\", \"test\", \"copy\", \"include\", \"address\", \"information\", \"send\", \"set\", \"source\", \"list\", \"run\", \"use\", \"support\", \"system\", \"mail\", \"also\", \"email\", \"follow\", \"may\", \"problem\", \"government\", \"public\", \"gun\", \"attack\", \"country\", \"israeli\", \"war\", \"security\", \"protect\", \"soldier\", \"private\", \"publish\", \"crime\", \"village\", \"character\", \"military\", \"land\", \"weapon\", \"police\", \"citizen\", \"policy\", \"terrorist\", \"turkish\", \"bomb\", \"criminal\", \"political\", \"terrorism\", \"withdraw\", \"occupy\", \"population\", \"encryption\", \"drug\", \"kill\", \"state\", \"american\", \"force\", \"murder\", \"risk\", \"reality\", \"people\", \"report\", \"issue\", \"member\", \"death\", \"right\", \"child\", \"year\", \"law\", \"area\", \"case\", \"number\", \"group\", \"ax\", \"max\", \"patient\", \"di_di\", \"orbit\", \"satellite\", \"tumor\", \"neurologist\", \"moon\", \"spacecraft\", \"lunar\", \"radiation\", \"diet\", \"distant\", \"comet\", \"chemical\", \"cure\", \"discharge\", \"meat\", \"plot\", \"viral\", \"km\", \"venus\", \"gehrel\", \"therapy\", \"hypertension\", \"cancer\", \"planetary\", \"atmosphere\", \"pregnancy\", \"neurologic\", \"shuttle\", \"flight\", \"launch\", \"mission\", \"disease\", \"food\", \"eeg\", \"eat\", \"doctor\", \"fuel\", \"solar\", \"space\", \"dn\", \"treatment\", \"taste\", \"earth\", \"pain\", \"study\", \"research\"], \"Freq\": [11664.0, 5638.0, 1462.0, 1238.0, 6412.0, 9742.0, 5665.0, 3612.0, 3576.0, 2092.0, 5368.0, 2320.0, 1908.0, 1628.0, 2424.0, 2362.0, 11759.0, 2106.0, 5572.0, 1551.0, 4433.0, 5494.0, 2476.0, 1114.0, 1988.0, 1664.0, 1236.0, 6165.0, 6277.0, 1519.0, 2105.839771590441, 1514.314132228263, 1296.779489839612, 1054.6365908685168, 787.3975011397224, 804.4879714329077, 755.7357150874192, 750.3156651076123, 645.9871062022811, 636.7177117075277, 622.1478798153977, 533.0986670283446, 517.3359028517326, 513.9072023699287, 491.6134557091081, 500.1424973507439, 471.5817029817251, 466.57690995273447, 449.6603424189566, 421.1701930217615, 393.16278426536246, 361.88809394908, 341.05382724496985, 338.72031737430257, 340.0929588542323, 335.1988429477656, 333.54907317882305, 321.8257497815219, 321.995394487833, 278.3364470629719, 2393.9298376559505, 710.4779009829023, 2322.3741585972143, 705.5080433440875, 693.8276230575286, 861.5434313160417, 463.5183052525443, 685.6968061543242, 864.6901970701106, 1111.7305667773849, 1044.783326971932, 659.2285545602998, 3648.089570832027, 1286.587130078273, 3627.6524643530743, 1448.5834698051672, 2769.3048309176643, 781.2459692507482, 1043.5619754088304, 1407.698547198565, 1575.805193118547, 2343.737714204377, 3542.216564455929, 3024.815629520545, 2194.269469909971, 2192.0914112352943, 1494.3887663123296, 1068.1453933482137, 1793.6083509160842, 1228.9737362076212, 1251.4784500445728, 1183.6782447847982, 1012.1130920113385, 1317.7616271148822, 1164.485264001416, 1216.3671893726325, 1137.9241739467027, 1116.973004330314, 1069.270745168782, 1663.5547398085923, 1519.3979413155625, 1154.5854587389504, 837.2569133694401, 503.5401018107895, 491.1639786078014, 471.4730120866321, 468.9873506676212, 465.48744922715866, 430.0135261850037, 463.98659000972157, 412.95282740242425, 403.6328371089398, 388.96913408849116, 387.94183341913765, 383.126739409839, 369.5318669840949, 349.88953303275355, 343.8608847100891, 340.21532035830717, 337.8922566682323, 309.6426035522917, 301.33752832498186, 300.5702079233026, 298.7997417134456, 294.76772893262023, 300.38753495129936, 288.06629581926984, 284.55829384213104, 283.14005422883747, 5046.045945094043, 917.0417405730287, 443.42461034746304, 974.8720589000811, 2891.449703270222, 1394.520420111972, 464.8693724088078, 1472.1758040619102, 514.9188904601721, 2057.639157359732, 1222.3916466823273, 1062.5964686381203, 775.7321633226132, 1250.508726884621, 952.3808014194326, 2855.6324172622144, 1959.780213145688, 2448.4576498413617, 1085.58450033537, 1608.296987571197, 2577.1840999569868, 2181.4921367724023, 2521.8919497884876, 1760.507832703885, 2648.7015076889015, 850.449283041641, 1531.6162508022169, 1901.857149543647, 3236.6863584755956, 2787.2535565955527, 2123.088441327571, 2586.0909335302126, 1717.9666316582736, 1723.9592678124536, 1145.4775205378057, 1178.0206635405448, 2319.646560270821, 1518.3806936816031, 1269.1143831036818, 1204.5324650079328, 1049.219725768328, 1026.4958319667865, 1009.5057367240236, 813.2682446773227, 733.4426690480976, 687.127411123033, 654.6142684972258, 647.3005164323689, 588.9246137386259, 556.9449126194454, 539.5270818978388, 526.6824042472718, 481.46299524275713, 439.61549132143085, 422.1014145359848, 421.21379644418823, 414.7693564642045, 404.3732746726284, 396.9143302998107, 385.5965857084403, 375.85128804408964, 461.827514566577, 346.01855225213984, 332.9219995733863, 333.9385410676005, 306.7268850199974, 318.7786558401976, 2273.1401496819985, 921.4270541441878, 1866.4733220992605, 484.6251346947825, 1340.7986970197182, 2905.324927385368, 7663.0793441439555, 2175.602396134478, 786.5078205381814, 1218.2606307365395, 823.5081673000831, 2278.4906383153207, 4538.778408429205, 3930.0809073404803, 1829.6748995234832, 1624.9439783743057, 2285.4723707607045, 1668.449431669623, 2267.20163638726, 2342.972654726945, 1482.2852664204806, 1463.7849256065397, 2248.989168694388, 1599.7002180709649, 1294.9333315391943, 1633.355030445627, 1408.7329584443064, 1489.167320425727, 1157.1871035164515, 1163.1864507976125, 1908.1470456511947, 1550.5593505213171, 1184.257776772511, 897.1840400358947, 829.9638618508934, 801.6980182006638, 722.9761923227626, 684.5659374532355, 670.413088497311, 661.5424308431117, 617.1817967255229, 599.7055406662448, 589.6220877982227, 581.0558972220978, 541.5788712052517, 497.7821627141418, 492.9477433762124, 480.7915036408889, 465.6332071520111, 440.8247004048745, 378.3177553814323, 362.71558306838756, 357.80717766229895, 343.8599312413685, 344.4065677482445, 331.53171346774315, 309.95393846101916, 307.34437719027704, 303.360675881814, 292.2316168333107, 1226.1579175449467, 2048.646873178865, 679.569950075075, 483.1147798184909, 759.5825345441623, 718.0239553458503, 1316.1534669474638, 834.2277808267204, 1176.9416382454092, 1128.3896507525794, 1092.3350181365308, 970.7860949328457, 881.1916044033667, 1209.0877934571818, 1564.3205156901854, 839.6460935067262, 1206.8583898937607, 863.44582476831, 1179.809359351346, 679.3389414629182, 758.7675920051414, 875.856003520897, 679.4463288114366, 1627.6510719480782, 1235.273506954245, 1130.8416056464134, 709.4478707673854, 667.4198688053322, 552.1211660099782, 542.5924888618214, 532.5762277216263, 511.3278647581829, 510.64670404332367, 492.894189564177, 482.18850968573855, 455.35945385402533, 443.26790455779064, 447.17513963931924, 412.84401214488366, 400.22391329082745, 386.46027653487596, 386.96736965639377, 342.2334045559211, 341.19159540497174, 309.9657731928674, 306.87500058924275, 303.76717102028755, 292.4122085609269, 286.816388003532, 281.3451770522674, 278.60315267636275, 276.36072068457895, 262.7094327140128, 566.1449124719109, 500.7425572904703, 855.8069468172614, 1228.8083040529364, 337.76296506734764, 550.9489233630446, 389.0116090869586, 400.30869326015534, 382.9707748283767, 1353.7202475983502, 579.7586787248736, 668.8973698275785, 438.0431000422629, 506.6250105940169, 694.2357262241259, 511.9736133378315, 684.3592023791862, 531.8284619011318, 477.3624342730854, 539.9747315102909, 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              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el581401041611074085060759082\", ldavis_el581401041611074085060759082_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_el581401041611074085060759082\", ldavis_el581401041611074085060759082_data);\n",
              "      });\n",
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              "    // 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_el581401041611074085060759082\", ldavis_el581401041611074085060759082_data);\n",
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              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing LDA Mallet Model.\n",
        "### Gensim uses a Variational Bayes sampling method which is faster but less precise than Mallet's Gibbs Sampling"
      ],
      "metadata": {
        "id": "QGdJDNotMQ2_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models.wrappers import LdaMallet"
      ],
      "metadata": {
        "id": "7aX6EKBlb762"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
        "!unzip mallet-2.0.8.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJTtPhJoc8h6",
        "outputId": "3223fcf9-e2d0-4884-8717-3f038fc1aae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-01 11:48:58--  http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
            "Resolving mallet.cs.umass.edu (mallet.cs.umass.edu)... 128.119.246.70\n",
            "Connecting to mallet.cs.umass.edu (mallet.cs.umass.edu)|128.119.246.70|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://mallet.cs.umass.edu/dist/mallet-2.0.8.zip [following]\n",
            "--2022-05-01 11:48:58--  https://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
            "Connecting to mallet.cs.umass.edu (mallet.cs.umass.edu)|128.119.246.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16184794 (15M) [application/zip]\n",
            "Saving to: ‘mallet-2.0.8.zip’\n",
            "\n",
            "mallet-2.0.8.zip    100%[===================>]  15.43M  44.2MB/s    in 0.3s    \n",
            "\n",
            "2022-05-01 11:48:59 (44.2 MB/s) - ‘mallet-2.0.8.zip’ saved [16184794/16184794]\n",
            "\n",
            "Archive:  mallet-2.0.8.zip\n",
            "   creating: mallet-2.0.8/\n",
            "   creating: mallet-2.0.8/bin/\n",
            "  inflating: mallet-2.0.8/bin/classifier2info  \n",
            "  inflating: mallet-2.0.8/bin/csv2classify  \n",
            "  inflating: mallet-2.0.8/bin/csv2vectors  \n",
            "  inflating: mallet-2.0.8/bin/mallet  \n",
            "  inflating: mallet-2.0.8/bin/mallet.bat  \n",
            "  inflating: mallet-2.0.8/bin/mallethon  \n",
            "  inflating: mallet-2.0.8/bin/prepend-license.sh  \n",
            "  inflating: mallet-2.0.8/bin/svmlight2vectors  \n",
            "  inflating: mallet-2.0.8/bin/text2classify  \n",
            "  inflating: mallet-2.0.8/bin/text2vectors  \n",
            "  inflating: mallet-2.0.8/bin/vectors2classify  \n",
            "  inflating: mallet-2.0.8/bin/vectors2info  \n",
            "  inflating: mallet-2.0.8/bin/vectors2topics  \n",
            "  inflating: mallet-2.0.8/bin/vectors2vectors  \n",
            "  inflating: mallet-2.0.8/build.xml  \n",
            "   creating: mallet-2.0.8/class/\n",
            "   creating: mallet-2.0.8/class/cc/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/classify/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/AdaBoost.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/AdaBoostM2.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/AdaBoostM2Trainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/AdaBoostTrainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/BaggingClassifier.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/BaggingTrainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/BalancedWinnow.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/BalancedWinnowTrainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/Boostable.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/C45$Node.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/C45.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/C45Trainer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/Classification.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/classify/Classifier.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizable$ByGradient.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizable$ByGradientValue.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizable$ByHessian.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizable$ByValue.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizable$ByVotedPerceptron.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizableCollection$ByGradientValue.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizableCollection.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizationException.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizer$ByBatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/Optimizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizerEvaluator$ByBatchGradient.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizerEvaluator$ByGradient.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OptimizerEvaluator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/OrthantWiseLimitedMemoryBFGS.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/StochasticMetaAscent.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/optimize/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/tests/TestOptimizable$SimplePoly.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/optimize/tests/TestOptimizable$WrongSimplePoly.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/pipe/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AddClassifierTokenPredictions$TokenClassifiers.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AddClassifierTokenPredictions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Array2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AugmentableFeatureVectorAddConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/AugmentableFeatureVectorLogScale.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$BranchingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$GateKeepingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe$PeekingInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/BranchingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequence2CharNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceArray2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceLowercase.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$ParserGetter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML$TagStripper.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveHTML.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceRemoveUUEncodedBlocks.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSequenceReplace.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/CharSubsequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Classification2ConfidencePredictingFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Csv2Array.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Csv2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Directory2FileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureCountPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureDocFreqPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequence2AugmentableFeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequence2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureSequenceConvolution.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureValueString2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorSequence2FeatureVectors$FeatureVectorIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FeatureVectorSequence2FeatureVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Filename2CharSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FilterEmptyFeatureVectors$FilteringPipeInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FilterEmptyFeatureVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/FixedVocabTokenizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Input2CharSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/InstanceListTrimFeaturesByCount.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/iterator/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ArrayDataAndTargetIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ArrayIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ConcatenatedInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/CsvIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/DBInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/EmptyInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileListIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/FileUriIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/LineGroupIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/LineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/ParenGroupIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PatternMatchIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PipeExtendedIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/PipeInputIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/RandomFeatureVectorIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/RandomTokenSequenceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SegmentIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SelectiveFileLineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/SimpleFileLineIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/StringArrayIterator.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/iterator/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/tests/TestPatternMatchIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/iterator/UnlabeledFileIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/LineGroupString2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/MakeAmpersandXMLFriendly.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/NGramPreprocessor$ReplacementSet.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/NGramPreprocessor.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Noop.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Pipe$SimplePipeInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Pipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PipeException.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PipeUtils.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintInput.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintInputAndTarget.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/PrintTokenSequenceFeatures.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SaveDataInSource.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SelectiveSGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SerialPipes$Predicate.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SerialPipes.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTaggerSentence2StringTokenization.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTaggerSentence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SimpleTokenizer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SourceLocation2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/StringAddNewLineDelimiter.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/StringList2FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/SvmLight2FeatureVectorAndLabel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2Double.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2Integer.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2Label.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Target2LabelSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TargetRememberLastLabel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TargetStringToFeatures.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestInstancePipe$Array2ArrayIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestInstancePipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestIterators.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils$1.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils$StupidPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestPipeUtils.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestRainbowStyle.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSGML2TokenSequence$Array2ArrayIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSGML2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tests/TestSpacePipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/Token2FeatureVector.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureSequenceWithBigrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2FeatureVectorSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2TokenInstances$TokenInstanceIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequence2TokenInstances.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceLowercase.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceMatchDataAndTarget.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceParseFeatureString.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveNonAlpha.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveStopPatterns.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/TokenSequenceRemoveStopwords.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tsf/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatchesAlignedWithOffsets.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/CountMatchesMatching.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/FeaturesInWindow.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/FeaturesOfFirstMention.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/LexiconMembership.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetFeatureConjunction.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/OffsetPropertyConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/RegexMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/SequencePrintingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/Target2BIOFormat.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestOffsetConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestOffsetFeatureConjunctions.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/tests/TestSequencePrintingPipe.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenFirstPosition.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenText.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharPrefix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextCharSuffix.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TokenTextNGrams.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TrieLexiconMembership$TrieLexicon.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/TrieLexiconMembership.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/tsf/WordVectors.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/pipe/ValueString2FeatureVector.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/regression/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/CoordinateDescent.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LeastSquares.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LinearRegression.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/LinearRegressionTrainer.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/regression/tui/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/tui/Regression.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/regression/tui/RegressionImporter.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/casutton/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/casutton/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/casutton/ner/ConllNer2003Sentence2TokenSequence.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/mccallum/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/ConllNer2003Sentence2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/TokenSequenceDocHeader.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/mccallum/ner/TUI.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/upenn/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/MaxEntShell.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/FeatureWindow.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/LengthBins.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/ListMember.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/LongRegexMatches.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/upenn/ner/NEPipes.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/ner/\n",
            "   creating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/EnronMessage2TokenSequence.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/enron/TUI.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/share/weili/ner/WordTransformation.class  \n",
            "   creating: mallet-2.0.8/class/cc/mallet/topics/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/AbstractTopicReports.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/DMROptimizable.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/DMRTopicModel.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalLDA$NCRPNode.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalLDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/HierarchicalPAM.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/JSONTopicReports.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LabeledLDA.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDA$1WordProb.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/topics/LDAHyper$Topication.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/topics/RTopicModel.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/topics/tui/\n",
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            "   creating: mallet-2.0.8/class/cc/mallet/types/\n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/types/InstanceList$NotYetSetPipe.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/types/MultiInstanceList$MultiIterator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/MultiInstanceList.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$Estimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$LaplaceEstimator.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$Logged.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/Multinomial$MAPEstimator.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/types/NormalizedDotProductMetric.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/NullLabel.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelFeatureCounts$Factory.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelFeatureCounts.class  \n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/PerLabelInfoGain$Factory.class  \n",
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            "  inflating: mallet-2.0.8/class/cc/mallet/types/PropertyHolder.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/types/tests/\n",
            "  inflating: mallet-2.0.8/class/cc/mallet/types/tests/TestAlphabet.class  \n",
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            "   creating: mallet-2.0.8/class/cc/mallet/util/\n",
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            "   creating: mallet-2.0.8/src/cc/mallet/util/resources/wn/\n",
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            "   creating: mallet-2.0.8/test/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade gensim==3.8"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "JGSKL01cdYcF",
        "outputId": "cbba3bce-37ef-42b2-b65b-02ee2efaa89f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gensim==3.8\n",
            "  Downloading gensim-3.8.0-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 24.2 MB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim==3.8) (1.4.1)\n",
            "Requirement already satisfied: smart-open>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from gensim==3.8) (6.0.0)\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim==3.8) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.7/dist-packages (from gensim==3.8) (1.21.6)\n",
            "Installing collected packages: gensim\n",
            "  Attempting uninstall: gensim\n",
            "    Found existing installation: gensim 3.6.0\n",
            "    Uninstalling gensim-3.6.0:\n",
            "      Successfully uninstalled gensim-3.6.0\n",
            "Successfully installed gensim-3.8.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gensim"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os       #importing os to set environment variable\n",
        "def install_java():\n",
        "  !apt-get install -y openjdk-8-jdk-headless -qq > /dev/null      #install openjdk\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     #set environment variable\n",
        "  !java -version       #check java version\n",
        "install_java()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_La3gRX4dcqV",
        "outputId": "af92ee04-2049-4974-d159-59b1c8951fca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "openjdk version \"11.0.15\" 2022-04-19\n",
            "OpenJDK Runtime Environment (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1)\n",
            "OpenJDK 64-Bit Server VM (build 11.0.15+10-Ubuntu-0ubuntu0.18.04.1, mixed mode, sharing)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['MALLET_HOME'] = '/content/mallet-2.0.8'\n",
        "mallet_path = '/content/mallet-2.0.8/bin/mallet' # you should NOT need to change this "
      ],
      "metadata": {
        "id": "dbdJyqbedQpy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LDA Mallet model."
      ],
      "metadata": {
        "id": "umBO4UFRT4iz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=20, id2word=id2word)"
      ],
      "metadata": {
        "id": "jrtW6-_LcFfI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Show Topics\n",
        "pprint(ldamallet.show_topics(formatted=False))\n",
        "\n",
        "# Compute Coherence Score\n",
        "coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
        "coherence_ldamallet = coherence_model_ldamallet.get_coherence()\n",
        "print('\\nCoherence Score: ', coherence_ldamallet)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gslb2_4YeBvc",
        "outputId": "bfb7f0c3-b862-4cb0-dd1f-b9c69283f27c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(8,\n",
            "  [('drive', 0.037224172237251),\n",
            "   ('card', 0.028393212457242505),\n",
            "   ('problem', 0.0262469538777974),\n",
            "   ('system', 0.024078338438149747),\n",
            "   ('driver', 0.01976346441905699),\n",
            "   ('work', 0.018824476290549754),\n",
            "   ('bit', 0.015090880636723378),\n",
            "   ('disk', 0.014353104250039125),\n",
            "   ('scsi', 0.013525900422544658),\n",
            "   ('machine', 0.01332468868072168)]),\n",
            " (15,\n",
            "  [('people', 0.01675617168311061),\n",
            "   ('leave', 0.014986886474334956),\n",
            "   ('time', 0.013571458307314432),\n",
            "   ('man', 0.013051080304733358),\n",
            "   ('back', 0.010365929811415013),\n",
            "   ('woman', 0.010220223970692312),\n",
            "   ('live', 0.009887182049040422),\n",
            "   ('day', 0.009699845968111236),\n",
            "   ('start', 0.009533325007285293),\n",
            "   ('happen', 0.009283543566046376)]),\n",
            " (1,\n",
            "  [('car', 0.03413368494927798),\n",
            "   ('bike', 0.013483337232300462),\n",
            "   ('power', 0.011207758235681929),\n",
            "   ('ride', 0.009463856574721933),\n",
            "   ('light', 0.009442589481295591),\n",
            "   ('engine', 0.008060228408583399),\n",
            "   ('ground', 0.007719954913761936),\n",
            "   ('turn', 0.007719954913761936),\n",
            "   ('wire', 0.007443482699219498),\n",
            "   ('water', 0.006805469896429255)]),\n",
            " (7,\n",
            "  [('ax', 0.18811802232854866),\n",
            "   ('line', 0.17109250398724082),\n",
            "   ('max', 0.15159489633173845),\n",
            "   ('host', 0.09677033492822966),\n",
            "   ('nntp_poste', 0.04322169059011164),\n",
            "   ('ca', 0.020414673046251993),\n",
            "   ('organization', 0.020135566188197767),\n",
            "   ('reply', 0.015191387559808613),\n",
            "   ('distribution_usa', 0.011323763955342903),\n",
            "   ('nntp_posting', 0.011044657097288677)]),\n",
            " (14,\n",
            "  [('game', 0.033892167096265684),\n",
            "   ('team', 0.028992608056579255),\n",
            "   ('year', 0.02792748652621264),\n",
            "   ('play', 0.026223292077626057),\n",
            "   ('player', 0.020045587201499692),\n",
            "   ('win', 0.01725496879193916),\n",
            "   ('good', 0.013399228852012014),\n",
            "   ('season', 0.0123341073216454),\n",
            "   ('hit', 0.010502098289414822),\n",
            "   ('fan', 0.0095647913426922)]),\n",
            " (11,\n",
            "  [('people', 0.038188761593016915),\n",
            "   ('bad', 0.02146635356626106),\n",
            "   ('make', 0.019734813444341658),\n",
            "   ('happen', 0.018430228420977728),\n",
            "   ('write', 0.01824047059939752),\n",
            "   ('point', 0.017315401219194004),\n",
            "   ('start', 0.01513318627102161),\n",
            "   ('wrong', 0.013259327782917052),\n",
            "   ('fire', 0.012761213501269006),\n",
            "   ('hear', 0.012429137313503641)]),\n",
            " (3,\n",
            "  [('mail', 0.019779114595943604),\n",
            "   ('include', 0.01847540647756691),\n",
            "   ('file', 0.018456782075875814),\n",
            "   ('software', 0.016892332333823777),\n",
            "   ('image', 0.016352224684782),\n",
            "   ('list', 0.015905239044195704),\n",
            "   ('version', 0.015476877805300505),\n",
            "   ('information', 0.013893803661557373),\n",
            "   ('system', 0.013707559644646415),\n",
            "   ('send', 0.012757715158400537)]),\n",
            " (0,\n",
            "  [('space', 0.021334645317112443),\n",
            "   ('launch', 0.00961624474461043),\n",
            "   ('design', 0.008565166830664639),\n",
            "   ('system', 0.00834153323195277),\n",
            "   ('project', 0.00780481259504428),\n",
            "   ('year', 0.006731371321227302),\n",
            "   ('earth', 0.006709007961356114),\n",
            "   ('satellite', 0.006619554521871366),\n",
            "   ('high', 0.006485374362644244),\n",
            "   ('mission', 0.006261740763932373)]),\n",
            " (19,\n",
            "  [('buy', 0.029961948857135254),\n",
            "   ('price', 0.025411777227854503),\n",
            "   ('line', 0.024852984220749847),\n",
            "   ('sell', 0.023895053351427584),\n",
            "   ('cost', 0.021606662941379952),\n",
            "   ('pay', 0.020276203400654585),\n",
            "   ('sale', 0.018573215188526115),\n",
            "   ('mail', 0.015087411191825656),\n",
            "   ('cheap', 0.012506319682818446),\n",
            "   ('good', 0.011708043958383225)]),\n",
            " (13,\n",
            "  [('question', 0.02270069972838307),\n",
            "   ('exist', 0.01653085820157214),\n",
            "   ('true', 0.013454931376252406),\n",
            "   ('reason', 0.013005234471965894),\n",
            "   ('argument', 0.01275340420556545),\n",
            "   ('claim', 0.012465598186822082),\n",
            "   ('evidence', 0.012123828539564334),\n",
            "   ('answer', 0.01073876207436188),\n",
            "   ('point', 0.00946162286618819),\n",
            "   ('atheist', 0.008993938085730218)])]\n",
            "\n",
            "Coherence Score:  0.5444989402784156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
        "    \"\"\"\n",
        "    Compute c_v coherence for various number of topics\n",
        "\n",
        "    Parameters:\n",
        "    ----------\n",
        "    dictionary : Gensim dictionary\n",
        "    corpus : Gensim corpus\n",
        "    texts : List of input texts\n",
        "    limit : Max num of topics\n",
        "\n",
        "    Returns:\n",
        "    -------\n",
        "    model_list : List of LDA topic models\n",
        "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
        "    \"\"\"\n",
        "    coherence_values = []\n",
        "    model_list = []\n",
        "    for num_topics in range(start, limit, step):\n",
        "        model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
        "        model_list.append(model)\n",
        "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
        "        coherence_values.append(coherencemodel.get_coherence())\n",
        "\n",
        "    return model_list, coherence_values"
      ],
      "metadata": {
        "id": "jcPW2t6qlZ5B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Can take a long time to run.\n",
        "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=6)"
      ],
      "metadata": {
        "id": "klilpbb5mO46"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Show graph\n",
        "limit=40; start=2; step=6;\n",
        "x = range(start, limit, step)\n",
        "plt.plot(x, coherence_values)\n",
        "plt.xlabel(\"Num Topics\")\n",
        "plt.ylabel(\"Coherence score\")\n",
        "plt.legend((\"coherence_values\"), loc='best')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "vjqNIEUbmVzU",
        "outputId": "40ee2d0a-276c-4654-8950-6686f9e1beef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the coherence scores\n",
        "for m, cv in zip(x, coherence_values):\n",
        "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILV9ZZnFrWlO",
        "outputId": "07bbfa25-f06f-4cb8-df81-2b691dfd8122"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num Topics = 2  has Coherence Value of 0.4047\n",
            "Num Topics = 8  has Coherence Value of 0.4654\n",
            "Num Topics = 14  has Coherence Value of 0.5341\n",
            "Num Topics = 20  has Coherence Value of 0.5492\n",
            "Num Topics = 26  has Coherence Value of 0.5312\n",
            "Num Topics = 32  has Coherence Value of 0.5387\n",
            "Num Topics = 38  has Coherence Value of 0.5308\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the model and print the topics\n",
        "optimal_model = model_list[3]\n",
        "model_topics = optimal_model.show_topics(formatted=False)\n",
        "pprint(optimal_model.print_topics(num_words=10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_MY8sYurZ4O",
        "outputId": "ae40d0e9-a87e-49a5-c688-ad619f02e8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0,\n",
            "  '0.051*\"line\" + 0.026*\"bit\" + 0.024*\"problem\" + 0.022*\"color\" + '\n",
            "  '0.021*\"image\" + 0.015*\"point\" + 0.013*\"monitor\" + 0.012*\"find\" + '\n",
            "  '0.012*\"screen\" + 0.009*\"quality\"'),\n",
            " (1,\n",
            "  '0.017*\"people\" + 0.012*\"government\" + 0.010*\"israeli\" + 0.010*\"war\" + '\n",
            "  '0.009*\"attack\" + 0.008*\"turkish\" + 0.008*\"state\" + 0.008*\"country\" + '\n",
            "  '0.008*\"world\" + 0.008*\"force\"'),\n",
            " (2,\n",
            "  '0.193*\"ax\" + 0.156*\"max\" + 0.152*\"line\" + 0.105*\"host\" + 0.044*\"nntp_poste\" '\n",
            "  '+ 0.034*\"reply\" + 0.032*\"organization\" + 0.011*\"nntp_posting\" + '\n",
            "  '0.009*\"distribution_usa\" + 0.009*\"ca\"'),\n",
            " (3,\n",
            "  '0.022*\"people\" + 0.016*\"man\" + 0.013*\"life\" + 0.011*\"religion\" + '\n",
            "  '0.011*\"love\" + 0.010*\"christian\" + 0.010*\"church\" + 0.009*\"world\" + '\n",
            "  '0.009*\"make\" + 0.008*\"word\"'),\n",
            " (4,\n",
            "  '0.026*\"gun\" + 0.025*\"law\" + 0.020*\"people\" + 0.020*\"state\" + 0.014*\"case\" + '\n",
            "  '0.011*\"make\" + 0.011*\"crime\" + 0.010*\"person\" + 0.010*\"weapon\" + '\n",
            "  '0.008*\"police\"'),\n",
            " (5,\n",
            "  '0.034*\"line\" + 0.031*\"buy\" + 0.026*\"price\" + 0.024*\"sell\" + 0.019*\"sale\" + '\n",
            "  '0.019*\"call\" + 0.017*\"cost\" + 0.015*\"include\" + 0.014*\"company\" + '\n",
            "  '0.013*\"pay\"'),\n",
            " (6,\n",
            "  '0.254*\"write\" + 0.172*\"article\" + 0.097*\"line\" + 0.043*\"organization\" + '\n",
            "  '0.019*\"opinion\" + 0.019*\"hear\" + 0.014*\"host\" + 0.013*\"read\" + '\n",
            "  '0.007*\"university\" + 0.006*\"delete\"'),\n",
            " (7,\n",
            "  '0.043*\"file\" + 0.023*\"number\" + 0.020*\"line\" + 0.018*\"read\" + 0.018*\"book\" '\n",
            "  '+ 0.016*\"program\" + 0.014*\"follow\" + 0.011*\"entry\" + 0.011*\"source\" + '\n",
            "  '0.010*\"author\"'),\n",
            " (8,\n",
            "  '0.027*\"window\" + 0.018*\"program\" + 0.017*\"version\" + 0.016*\"software\" + '\n",
            "  '0.015*\"application\" + 0.015*\"file\" + 0.014*\"run\" + 0.012*\"server\" + '\n",
            "  '0.012*\"include\" + 0.011*\"system\"'),\n",
            " (9,\n",
            "  '0.021*\"people\" + 0.014*\"kill\" + 0.013*\"happen\" + 0.012*\"child\" + '\n",
            "  '0.012*\"start\" + 0.011*\"fire\" + 0.011*\"day\" + 0.010*\"leave\" + 0.009*\"time\" + '\n",
            "  '0.009*\"live\"'),\n",
            " (10,\n",
            "  '0.036*\"car\" + 0.014*\"bike\" + 0.011*\"power\" + 0.010*\"ride\" + 0.009*\"light\" + '\n",
            "  '0.008*\"engine\" + 0.008*\"wire\" + 0.008*\"ground\" + 0.007*\"water\" + '\n",
            "  '0.006*\"mile\"'),\n",
            " (11,\n",
            "  '0.040*\"key\" + 0.016*\"system\" + 0.015*\"encryption\" + 0.013*\"chip\" + '\n",
            "  '0.013*\"technology\" + 0.012*\"government\" + 0.011*\"public\" + 0.011*\"security\" '\n",
            "  '+ 0.010*\"bit\" + 0.008*\"privacy\"'),\n",
            " (12,\n",
            "  '0.021*\"space\" + 0.011*\"system\" + 0.010*\"launch\" + 0.010*\"design\" + '\n",
            "  '0.009*\"project\" + 0.008*\"year\" + 0.008*\"technology\" + 0.007*\"base\" + '\n",
            "  '0.007*\"time\" + 0.007*\"satellite\"'),\n",
            " (13,\n",
            "  '0.019*\"question\" + 0.015*\"claim\" + 0.015*\"exist\" + 0.013*\"reason\" + '\n",
            "  '0.013*\"true\" + 0.012*\"argument\" + 0.011*\"evidence\" + 0.011*\"point\" + '\n",
            "  '0.009*\"truth\" + 0.008*\"word\"'),\n",
            " (14,\n",
            "  '0.034*\"game\" + 0.030*\"year\" + 0.029*\"team\" + 0.026*\"play\" + 0.020*\"player\" '\n",
            "  '+ 0.018*\"win\" + 0.016*\"good\" + 0.012*\"season\" + 0.011*\"hit\" + 0.010*\"run\"'),\n",
            " (15,\n",
            "  '0.013*\"drug\" + 0.011*\"study\" + 0.009*\"problem\" + 0.009*\"food\" + '\n",
            "  '0.008*\"effect\" + 0.008*\"doctor\" + 0.008*\"find\" + 0.008*\"people\" + '\n",
            "  '0.007*\"eat\" + 0.007*\"patient\"'),\n",
            " (16,\n",
            "  '0.021*\"make\" + 0.021*\"work\" + 0.016*\"year\" + 0.016*\"people\" + 0.013*\"money\" '\n",
            "  '+ 0.013*\"job\" + 0.008*\"pay\" + 0.008*\"time\" + 0.008*\"talk\" + '\n",
            "  '0.008*\"support\"'),\n",
            " (17,\n",
            "  '0.038*\"drive\" + 0.027*\"card\" + 0.027*\"problem\" + 0.025*\"system\" + '\n",
            "  '0.021*\"driver\" + 0.020*\"work\" + 0.017*\"run\" + 0.015*\"disk\" + 0.014*\"scsi\" + '\n",
            "  '0.014*\"memory\"'),\n",
            " (18,\n",
            "  '0.049*\"post\" + 0.029*\"mail\" + 0.026*\"group\" + 0.023*\"send\" + '\n",
            "  '0.022*\"information\" + 0.019*\"address\" + 0.019*\"list\" + 0.018*\"internet\" + '\n",
            "  '0.017*\"question\" + 0.015*\"message\"'),\n",
            " (19,\n",
            "  '0.048*\"good\" + 0.045*\"thing\" + 0.041*\"make\" + 0.038*\"time\" + 0.027*\"bad\" + '\n",
            "  '0.018*\"lot\" + 0.016*\"put\" + 0.016*\"back\" + 0.015*\"give\" + 0.014*\"long\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data):\n",
        "    # Init output\n",
        "    sent_topics_df = pd.DataFrame()\n",
        "\n",
        "    # Get main topic in each document\n",
        "    for i, row in enumerate(ldamodel[corpus]):\n",
        "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
        "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
        "        for j, (topic_num, prop_topic) in enumerate(row):\n",
        "            if j == 0:  # => dominant topic\n",
        "                wp = ldamodel.show_topic(topic_num)\n",
        "                topic_keywords = \", \".join([word for word, prop in wp])\n",
        "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
        "            else:\n",
        "                break\n",
        "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
        "\n",
        "    # Add original text to the end of the output\n",
        "    contents = pd.Series(texts)\n",
        "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
        "    return(sent_topics_df)\n",
        "\n",
        "\n",
        "df_topic_sents_keywords = format_topics_sentences(ldamodel=optimal_model, corpus=corpus, texts=data)\n",
        "\n",
        "# Format\n",
        "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
        "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
        "\n",
        "# Show\n",
        "df_dominant_topic.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "k43misYXrZ-Y",
        "outputId": "90095c97-8257-4376-d0a9-475d2087a783"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
              "0            0            10.0              0.1603   \n",
              "1            1            17.0              0.1414   \n",
              "2            2            19.0              0.1212   \n",
              "3            3             6.0              0.0919   \n",
              "4            4             7.0              0.1413   \n",
              "5            5             4.0              0.3095   \n",
              "6            6            18.0              0.1038   \n",
              "7            7            17.0              0.5048   \n",
              "8            8             8.0              0.1349   \n",
              "9            9            17.0              0.1459   \n",
              "\n",
              "                                            Keywords  \\\n",
              "0  car, bike, power, ride, light, engine, wire, g...   \n",
              "1  drive, card, problem, system, driver, work, ru...   \n",
              "2  good, thing, make, time, bad, lot, put, back, ...   \n",
              "3  write, article, line, organization, opinion, h...   \n",
              "4  file, number, line, read, book, program, follo...   \n",
              "5  gun, law, people, state, case, make, crime, pe...   \n",
              "6  post, mail, group, send, information, address,...   \n",
              "7  drive, card, problem, system, driver, work, ru...   \n",
              "8  window, program, version, software, applicatio...   \n",
              "9  drive, card, problem, system, driver, work, ru...   \n",
              "\n",
              "                                                Text  \n",
              "0  From: (wheres my thing) Subject: WHAT car is t...  \n",
              "1  From: (Guy Kuo) Subject: SI Clock Poll - Final...  \n",
              "2  From: (Thomas E Willis) Subject: PB questions....  \n",
              "3  From: (Joe Green) Subject: Re: Weitek P9000 ? ...  \n",
              "4  From: (Jonathan McDowell) Subject: Re: Shuttle...  \n",
              "5  From: (Foxvog Douglas) Subject: Re: Rewording ...  \n",
              "6  From: (brian manning delaney) Subject: Brain T...  \n",
              "7  From: (GRUBB) Subject: Re: IDE vs SCSI Organiz...  \n",
              "8  From: Subject: WIn 3.0 ICON HELP PLEASE! Organ...  \n",
              "9  From: (Stan Kerr) Subject: Re: Sigma Designs D...  "
            ],
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              "\n",
              "  <div id=\"df-66dfa5ca-9f1e-4db6-9b28-fe2e9208294e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Document_No</th>\n",
              "      <th>Dominant_Topic</th>\n",
              "      <th>Topic_Perc_Contrib</th>\n",
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              "      <th>0</th>\n",
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              "      <td>0.1603</td>\n",
              "      <td>car, bike, power, ride, light, engine, wire, g...</td>\n",
              "      <td>From: (wheres my thing) Subject: WHAT car is t...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>17.0</td>\n",
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              "      <td>From: (Thomas E Willis) Subject: PB questions....</td>\n",
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              "      <td>From: (Joe Green) Subject: Re: Weitek P9000 ? ...</td>\n",
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              "      <td>0.1413</td>\n",
              "      <td>file, number, line, read, book, program, follo...</td>\n",
              "      <td>From: (Jonathan McDowell) Subject: Re: Shuttle...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.3095</td>\n",
              "      <td>gun, law, people, state, case, make, crime, pe...</td>\n",
              "      <td>From: (Foxvog Douglas) Subject: Re: Rewording ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.1038</td>\n",
              "      <td>post, mail, group, send, information, address,...</td>\n",
              "      <td>From: (brian manning delaney) Subject: Brain T...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.5048</td>\n",
              "      <td>drive, card, problem, system, driver, work, ru...</td>\n",
              "      <td>From: (GRUBB) Subject: Re: IDE vs SCSI Organiz...</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.1349</td>\n",
              "      <td>window, program, version, software, applicatio...</td>\n",
              "      <td>From: Subject: WIn 3.0 ICON HELP PLEASE! Organ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0.1459</td>\n",
              "      <td>drive, card, problem, system, driver, work, ru...</td>\n",
              "      <td>From: (Stan Kerr) Subject: Re: Sigma Designs D...</td>\n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group top 5 sentences under each topic\n",
        "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
        "\n",
        "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
        "\n",
        "for i, grp in sent_topics_outdf_grpd:\n",
        "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
        "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
        "                                            axis=0)\n",
        "\n",
        "# Reset Index    \n",
        "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Format\n",
        "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
        "\n",
        "# Show\n",
        "sent_topics_sorteddf_mallet.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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              "   Topic_Num  Topic_Perc_Contrib  \\\n",
              "0        0.0              0.4812   \n",
              "1        1.0              0.7119   \n",
              "2        2.0              0.9712   \n",
              "3        3.0              0.5885   \n",
              "4        4.0              0.6067   \n",
              "\n",
              "                                            Keywords  \\\n",
              "0  line, bit, problem, color, image, point, monit...   \n",
              "1  people, government, israeli, war, attack, turk...   \n",
              "2  ax, max, line, host, nntp_poste, reply, organi...   \n",
              "3  people, man, life, religion, love, christian, ...   \n",
              "4  gun, law, people, state, case, make, crime, pe...   \n",
              "\n",
              "                                                Text  \n",
              "0  From: (Ronald W. Schmidt) Subject: outlining o...  \n",
              "1  From: Center for Policy Research Subject: Fina...  \n",
              "2  Subject: roman.bmp 12/14 From: (Cliff) Reply-T...  \n",
              "3  From: (Marida Ignacio) Subject: Refusing Divin...  \n",
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    {
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      "source": [
        "# Number of Documents for Each Topic\n",
        "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
        "\n",
        "# Percentage of Documents for Each Topic\n",
        "topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n",
        "\n",
        "# Topic Number and Keywords\n",
        "topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n",
        "\n",
        "# Concatenate Column wise\n",
        "df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n",
        "\n",
        "# Change Column names\n",
        "df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n",
        "\n",
        "# Show\n",
        "df_dominant_topics"
      ],
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        "id": "82_3lSVjr4Ma",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "         Dominant_Topic                                     Topic_Keywords  \\\n",
              "0.0                10.0  car, bike, power, ride, light, engine, wire, g...   \n",
              "1.0                17.0  drive, card, problem, system, driver, work, ru...   \n",
              "2.0                19.0  good, thing, make, time, bad, lot, put, back, ...   \n",
              "3.0                 6.0  write, article, line, organization, opinion, h...   \n",
              "4.0                 7.0  file, number, line, read, book, program, follo...   \n",
              "...                 ...                                                ...   \n",
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              "11313.0            10.0  car, bike, power, ride, light, engine, wire, g...   \n",
              "\n",
              "         Num_Documents  Perc_Documents  \n",
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              "1.0              508.0          0.0449  \n",
              "2.0              308.0          0.0272  \n",
              "3.0              687.0          0.0607  \n",
              "4.0              512.0          0.0453  \n",
              "...                ...             ...  \n",
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              "11311.0            NaN             NaN  \n",
              "11312.0            NaN             NaN  \n",
              "11313.0            NaN             NaN  \n",
              "\n",
              "[11314 rows x 4 columns]"
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              "<p>11314 rows × 4 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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}