LinkedinTopicModeling_Gensim.ipynb

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    "colab": {
      "name": "Skartik_Linkedin Gensim.ipynb",
      "provenance": [],
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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        "colab_type": "code",
        "outputId": "0c914e21-11e0-4348-f219-198dd7ba29c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import pandas as pd\n",
        "import nltk; nltk.download('stopwords')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 1
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      "cell_type": "code",
      "metadata": {
        "id": "YltvdS1M3jTu",
        "colab_type": "code",
        "colab": {}
      },
      "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'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rlj1A2p_3EPM",
        "colab_type": "code",
        "outputId": "e7162ead-fdef-4150-8d7d-9a55f7997bcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        }
      },
      "source": [
        "!pip install pyLDAvis"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyLDAvis\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a5/3a/af82e070a8a96e13217c8f362f9a73e82d61ac8fff3a2561946a97f96266/pyLDAvis-2.1.2.tar.gz (1.6MB)\n",
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            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyLDAvis) (0.16.0)\n",
            "Collecting funcy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/4b/6ffa76544e46614123de31574ad95758c421aae391a1764921b8a81e1eae/funcy-1.14.tar.gz (548kB)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->pyLDAvis) (45.2.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyLDAvis) (1.12.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyLDAvis) (1.3.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyLDAvis) (8.2.0)\n",
            "Building wheels for collected packages: pyLDAvis, funcy\n",
            "  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-2.1.2-py2.py3-none-any.whl size=97711 sha256=e5c4483bde4a6ea28ac0136a9907037e00e49f1f2c0c73b3f5e7817002535453\n",
            "  Stored in directory: /root/.cache/pip/wheels/98/71/24/513a99e58bb6b8465bae4d2d5e9dba8f0bef8179e3051ac414\n",
            "  Building wheel for funcy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for funcy: filename=funcy-1.14-py2.py3-none-any.whl size=32042 sha256=cc8ec60df2ed32114e48584c4b47870a3a4c9e780f1e28ece429f6bb4e97a173\n",
            "  Stored in directory: /root/.cache/pip/wheels/20/5a/d8/1d875df03deae6f178dfdf70238cca33f948ef8a6f5209f2eb\n",
            "Successfully built pyLDAvis funcy\n",
            "Installing collected packages: funcy, pyLDAvis\n",
            "Successfully installed funcy-1.14 pyLDAvis-2.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8RwLLD526rm",
        "colab_type": "code",
        "colab": {}
      },
      "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 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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMm-MFBZ0pG5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1=pd.read_excel('/content/ShiladithData_March182020.xlsx')\n",
        "df2=pd.read_excel('/content/SkartikData_March182020.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHHAzz0500aJ",
        "colab_type": "code",
        "outputId": "e4c16efa-8c21-484b-c2e6-b17cebed42d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "df2.head(1)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Contact</th>\n",
              "      <th>Top Skills</th>\n",
              "      <th>Languages</th>\n",
              "      <th>Certifications</th>\n",
              "      <th>Honors-Awards</th>\n",
              "      <th>Publications</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Experience</th>\n",
              "      <th>Education</th>\n",
              "      <th>Designation_1</th>\n",
              "      <th>ExpDurationInMonths_1</th>\n",
              "      <th>Experience_Summary_1</th>\n",
              "      <th>Designation_2</th>\n",
              "      <th>ExpDurationInMonths_2</th>\n",
              "      <th>Experience_Summary_2</th>\n",
              "      <th>Education_1</th>\n",
              "      <th>EduDurationInMonths_1</th>\n",
              "      <th>Education_2</th>\n",
              "      <th>EduDurationInMonths_2</th>\n",
              "      <th>URL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>['www.linkedin.com/in/srihari-j\\n(LinkedIn)', ...</td>\n",
              "      <td>['Machine Learning', 'Deep Learning', 'Angular...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['Sequence Models', 'Convolutional Neural Netw...</td>\n",
              "      <td>['Wackiest Idea Award', 'Winner - Programming ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>['A versatile, self-driven and passionate indi...</td>\n",
              "      <td>['Arizona State University', '5 months', 'Teac...</td>\n",
              "      <td>['Arizona State University', 'Master of Scienc...</td>\n",
              "      <td>Teaching Assistant</td>\n",
              "      <td>5.0</td>\n",
              "      <td>['Teaching Assistant for CSE205 - Object-Orien...</td>\n",
              "      <td>Research Assistant</td>\n",
              "      <td>4.0</td>\n",
              "      <td>['Developing automated memory allocation and p...</td>\n",
              "      <td>Master of Science - MS,Computer Science·(2019-...</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Bachelor of Technology,Information Technology·...</td>\n",
              "      <td>48.0</td>\n",
              "      <td>www.linkedin.com/in/srihari-j</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...                            URL\n",
              "0           0  ...  www.linkedin.com/in/srihari-j\n",
              "\n",
              "[1 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASLgrAzy1CUQ",
        "colab_type": "code",
        "outputId": "b1e038c8-9329-4276-d821-9fdf4d9dd74b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df1.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(374, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNEnskjh1Ef2",
        "colab_type": "code",
        "outputId": "f0f39abe-33a9-4e5b-d1c6-f7c27e22c075",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df2.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(390, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nmuzn30X08Oy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_final = pd.concat([df1, df2],ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2B9nylb1Bfu",
        "colab_type": "code",
        "outputId": "ce7a7d58-6c3e-4f23-e6f0-b864e839ddc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_final.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(764, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRs1hCKb1Kao",
        "colab_type": "code",
        "outputId": "f962455f-36de-4686-897a-5217609fc5da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(df_final['URL'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "764"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZmjXGAd1RyX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_dup = df_final.drop_duplicates('URL', keep='last')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8Pt3UCy1w1o",
        "colab_type": "code",
        "outputId": "b43d28b4-fe28-4eb5-b051-9c19660b742c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_dup.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(748, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkoaA58812A7",
        "colab_type": "code",
        "outputId": "74daecde-4213-4f7b-bb66-8c2c9fd8b953",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        }
      },
      "source": [
        "!python3 -m spacy download en"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_sm==2.2.5) (2.2.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (45.2.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.21.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.6.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n",
            "Requirement already satisfied: srsly<1.1.0,>=0.1.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n",
            "Requirement already satisfied: thinc<7.4.0,>=7.3.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.3.1)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2019.11.28)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.8)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.5.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<7.4.0,>=7.3.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (4.28.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n",
            "/usr/local/lib/python3.6/dist-packages/spacy/data/en\n",
            "You can now load the model via spacy.load('en')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zj-_fQ-P2wKf",
        "colab_type": "code",
        "outputId": "ee5f4100-bf99-4d0f-dcf2-5f1c3c40829e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "df_dup.columns"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Contact', 'Top Skills', 'Languages', 'Certifications',\n",
              "       'Honors-Awards', 'Publications', 'Summary', 'Experience', 'Education',\n",
              "       'Designation_1', 'ExpDurationInMonths_1', 'Experience_Summary_1',\n",
              "       'Designation_2', 'ExpDurationInMonths_2', 'Experience_Summary_2',\n",
              "       'Education_1', 'EduDurationInMonths_1', 'Education_2',\n",
              "       'EduDurationInMonths_2', 'URL'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEl_7B2hyxLx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#del df_dup['test']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th8nFvyUlqM_",
        "colab_type": "code",
        "outputId": "7ad8ad29-95f8-40c8-ff03-aa8129628938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "df_dup.columns"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Contact', 'Top Skills', 'Languages', 'Certifications',\n",
              "       'Honors-Awards', 'Publications', 'Summary', 'Experience', 'Education',\n",
              "       'Designation_1', 'ExpDurationInMonths_1', 'Experience_Summary_1',\n",
              "       'Designation_2', 'ExpDurationInMonths_2', 'Experience_Summary_2',\n",
              "       'Education_1', 'EduDurationInMonths_1', 'Education_2',\n",
              "       'EduDurationInMonths_2', 'URL'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJHfaAXjqAiE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = df_dup.assign(ColumnA = df_dup[\"Summary\"].astype(str) + ', ' + \\\n",
        "  df_dup[\"Experience_Summary_1\"].astype(str) + ', ' +df_dup['Experience_Summary_2'].astype(str))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2FTEIvRqqqL",
        "colab_type": "code",
        "outputId": "f54bd9ac-1954-47d0-dfa6-151650216d2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "df2['ColumnA'][1]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"['Top Skills', 'Entrepreneurship', 'Seed Capital', 'Trading', 'Languages', 'English', 'Vivek is extremely passionate about financial markets and educating\\\\npeople on various aspects of finance, necessary for inclusive\\\\ndevelopment in society. ', 'He started his career as analyst and went on to build one of the\\\\nlargest financial market trading firms in India. He loves technology\\\\nand uses the same to its full potential in building his business\\\\nenterprises. Currently he is full time involved in elearnmarkets.com\\\\nand stockedge which are truly revolutionizing the way financial\\\\nmarket is approached in India, particularly by youngsters. '], ['Empowering the individual retail investors, particularly first-timers like', 'millennials with right learning, accurate data and effective analytics for', 'independent financial decisions.', 'Over 1 million downloads and active user base of 200,000 with a rating of 4.7', '(highest rated finance app in India, in this category) speaks a lot about our', 'dedication and effort.  ', 'Elearnmarkets'], ['Co founder of elearnmarkets.com, a dedicated BFSI elearning market place', 'in India with over 100+ practical oriented courses by industry experts. I has', 'collaboration with Industry leaders like NSE, MCX, NCDEX, ICICI DIRECT', 'etc and offers joint programs with them on online mode with lots of interactive', 'learning tools. Currently has over 80000 users in the portal. ', 'Page 1 of 3', 'Fincubation.com']\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cs39cM27stMc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = df2.ColumnA.values\n",
        "data = [re.sub('\\S*@\\S*\\s?', '', sent) for sent in data]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bUO8_zvnsxeC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def b(text):\n",
        "    for ch in ['\\\\n','.',\"\\'\",'\\\\s','[]','[]','[',']','-','\"','=>',':','\\\\uf','=','(',')','\\\\xa0','•','&','.',',',\"'\"]:\n",
        "        if ch in text:\n",
        "            text = text.replace(ch,\" \")\n",
        "    return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBvgfMD65Qjr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = [b(sent) for sent in data]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oosspUjJzp2e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2d7bbc28-04ec-4639-d2f4-4fa0180250bc"
      },
      "source": [
        "type(data)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0NP9XfLz7Qy",
        "colab_type": "text"
      },
      "source": [
        "# **Tokenize words and Clean-up text**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UxM3mOfzraW",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIrl5ELB0SIe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_orig = list(sent_to_words(data))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKuWE9IT0YqW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2085d818-7d64-467e-8770-08932d001310"
      },
      "source": [
        "data_orig[:1]"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['nan', 'keventer', 'agro', 'nan']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tR0IK0eSGsIH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a15c5dda-60ee-4846-9765-bb160044f48f"
      },
      "source": [
        "len(data_orig)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xn9gaAvAAc0-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_words=data_orig"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUJTXCDTEU_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i=0\n",
        "while i < len(data_words):\n",
        "    try:\n",
        "      data_words[i].remove('nan')\n",
        "    except:\n",
        "      i+=1\n",
        "      continue\n",
        "    i+=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qpksI2ywGmVx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "344c5b79-c2ad-4061-cc51-1c1912e400bc"
      },
      "source": [
        "data_words[:1]"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['keventer', 'agro']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BvFXHdf2Guu7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1fb7f727-c597-4df4-d8d6-bb161676f5fa"
      },
      "source": [
        "len(test)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "748"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxLHf2Td0m7d",
        "colab_type": "text"
      },
      "source": [
        "# **Creating Bigram and Trigram Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZyUkm1I0hfh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "outputId": "643dfed4-9c6a-4453-a522-a2d6dbd5fd15"
      },
      "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[1]]])"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "['top', 'skills', 'seed', 'capital', 'trading', 'languages_english', 'vivek', 'is', 'extremely', 'passionate_about', 'financial', 'markets', 'and', 'educating', 'people', 'on', 'various', 'aspects', 'of', 'finance', 'necessary', 'for', 'inclusive', 'development', 'in', 'society', 'he', 'started', 'his', 'career', 'as', 'analyst', 'and', 'went', 'on', 'to', 'build', 'one', 'of', 'the', 'largest', 'financial', 'market', 'trading', 'firms', 'in', 'india', 'he', 'loves', 'technology', 'and', 'uses', 'the', 'same', 'to', 'its', 'full', 'potential', 'in', 'building', 'his', 'business', 'enterprises', 'currently', 'he', 'is', 'full', 'time', 'involved', 'in', 'elearnmarkets', 'com', 'and', 'stockedge', 'which', 'are', 'truly', 'revolutionizing', 'the', 'way', 'financial', 'market', 'is', 'approached', 'in', 'india', 'particularly', 'by', 'youngsters', 'empowering', 'the', 'individual', 'retail', 'investors', 'particularly', 'first', 'timers', 'like', 'millennials', 'with', 'right', 'learning', 'accurate', 'data', 'and', 'effective', 'analytics', 'for', 'independent', 'financial', 'decisions', 'over', 'million', 'downloads', 'and', 'active', 'user', 'base', 'of', 'with', 'rating', 'of', 'highest', 'rated', 'finance', 'app', 'in', 'india', 'in', 'this', 'category', 'speaks', 'lot', 'about', 'our', 'dedication', 'and', 'effort', 'elearnmarkets', 'co', 'founder', 'of', 'elearnmarkets', 'com', 'dedicated', 'bfsi', 'elearning', 'market', 'place', 'in', 'india', 'with', 'over', 'practical', 'oriented', 'courses', 'by', 'industry', 'experts', 'has', 'collaboration', 'with', 'industry', 'leaders', 'like', 'nse', 'mcx', 'ncdex', 'icici', 'direct', 'etc', 'and', 'offers', 'joint', 'programs', 'with', 'them', 'on', 'online', 'mode', 'with', 'lots', 'of', 'interactive', 'learning', 'tools', 'currently', 'has', 'over', 'users', 'in', 'the', 'portal', 'page', 'of', 'fincubation', 'com']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIUCuDZq245I",
        "colab_type": "text"
      },
      "source": [
        "# **Remove Stopwords, Make Bigrams and Lemmatize**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Al9L9toO21d5",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWZdzFA942hL",
        "colab_type": "text"
      },
      "source": [
        "# **Call functions in order**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZAlFgss4y8Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "158734ce-716d-42cd-8078-03447b02d88f"
      },
      "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_orig = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
        "\n",
        "print(data_lemmatized_orig[:2])"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[], ['top', 'skill', 'seed', 'capital', 'trading', 'extremely', 'passionate', 'financial', 'market', 'educate', 'people', 'various', 'aspect', 'finance', 'necessary', 'society', 'start', 'career', 'analyst', 'go', 'build', 'large', 'financial', 'market', 'trading', 'firm', 'love', 'technology', 'use', 'full', 'potential', 'building', 'business', 'enterprise', 'currently', 'full', 'time', 'involve', 'elearnmarket', 'com', 'truly', 'revolutionize', 'way', 'financial', 'market', 'approach', 'particularly', 'youngster', 'empower', 'individual', 'retail', 'investor', 'particularly', 'first', 'timer', 'millennial', 'right', 'learn', 'accurate', 'datum', 'effective', 'analytic', 'independent', 'financial', 'decision', 'download', 'active', 'user', 'base', 'rate', 'high', 'rate', 'finance', 'category', 'speak', 'effort', 'elearnmarket', 'founder', 'elearnmarket', 'com', 'dedicate', 'elearne', 'market', 'place', 'orient', 'course', 'industry', 'expert', 'collaboration', 'industry', 'leader', 'icici', 'direct', 'offer', 'joint', 'program', 'online', 'mode', 'lot', 'interactive', 'learning', 'tool', 'currently', 'user', 'portal', 'page', 'fincubation', 'com']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNl-C2JVHlgj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_lemmatized=[x for x in data_lemmatized_orig if x]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCWfAuR6HRDw",
        "colab_type": "text"
      },
      "source": [
        "# **Create the Dictionary and Corpus needed for Topic Modeling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKTr3SwKHXZz",
        "colab_type": "text"
      },
      "source": [
        "The two main inputs to the LDA topic model are the dictionary(id2word) and the corpus. Let’s create them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzfkCrAp58y1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "4a0dcba5-63c1-4125-ea84-275e010327e7"
      },
      "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])"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 1), (8, 1), (9, 1), (10, 1), (11, 1), (12, 1), (13, 1), (14, 3), (15, 1), (16, 2), (17, 1), (18, 1), (19, 1), (20, 1), (21, 1), (22, 1), (23, 1), (24, 1), (25, 1), (26, 3), (27, 1), (28, 1), (29, 1), (30, 1), (31, 2), (32, 4), (33, 1), (34, 1), (35, 1), (36, 1), (37, 2), (38, 1), (39, 1), (40, 1), (41, 1), (42, 1), (43, 2), (44, 1), (45, 1), (46, 1), (47, 1), (48, 1), (49, 1), (50, 1), (51, 1), (52, 1), (53, 1), (54, 4), (55, 1), (56, 1), (57, 1), (58, 1), (59, 1), (60, 1), (61, 1), (62, 2), (63, 1), (64, 1), (65, 1), (66, 1), (67, 1), (68, 1), (69, 2), (70, 1), (71, 1), (72, 1), (73, 1), (74, 1), (75, 1), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 1), (82, 1), (83, 2), (84, 1), (85, 1), (86, 2), (87, 1), (88, 1), (89, 1)]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVxaoS9mIooq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a8507d93-0cc3-41cf-f9bb-1234a5884281"
      },
      "source": [
        "id2word[83]"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'trading'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "casXYWOpI1GB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1654
        },
        "outputId": "7d869df9-6d75-4d69-aa60-9891bd3f78f1"
      },
      "source": [
        "# Human readable format of corpus (term-frequency)\n",
        "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[('accurate', 1),\n",
              "  ('active', 1),\n",
              "  ('analyst', 1),\n",
              "  ('analytic', 1),\n",
              "  ('approach', 1),\n",
              "  ('aspect', 1),\n",
              "  ('base', 1),\n",
              "  ('build', 1),\n",
              "  ('building', 1),\n",
              "  ('business', 1),\n",
              "  ('capital', 1),\n",
              "  ('career', 1),\n",
              "  ('category', 1),\n",
              "  ('collaboration', 1),\n",
              "  ('com', 3),\n",
              "  ('course', 1),\n",
              "  ('currently', 2),\n",
              "  ('datum', 1),\n",
              "  ('decision', 1),\n",
              "  ('dedicate', 1),\n",
              "  ('direct', 1),\n",
              "  ('download', 1),\n",
              "  ('educate', 1),\n",
              "  ('effective', 1),\n",
              "  ('effort', 1),\n",
              "  ('elearne', 1),\n",
              "  ('elearnmarket', 3),\n",
              "  ('empower', 1),\n",
              "  ('enterprise', 1),\n",
              "  ('expert', 1),\n",
              "  ('extremely', 1),\n",
              "  ('finance', 2),\n",
              "  ('financial', 4),\n",
              "  ('fincubation', 1),\n",
              "  ('firm', 1),\n",
              "  ('first', 1),\n",
              "  ('founder', 1),\n",
              "  ('full', 2),\n",
              "  ('go', 1),\n",
              "  ('high', 1),\n",
              "  ('icici', 1),\n",
              "  ('independent', 1),\n",
              "  ('individual', 1),\n",
              "  ('industry', 2),\n",
              "  ('interactive', 1),\n",
              "  ('investor', 1),\n",
              "  ('involve', 1),\n",
              "  ('joint', 1),\n",
              "  ('large', 1),\n",
              "  ('leader', 1),\n",
              "  ('learn', 1),\n",
              "  ('learning', 1),\n",
              "  ('lot', 1),\n",
              "  ('love', 1),\n",
              "  ('market', 4),\n",
              "  ('millennial', 1),\n",
              "  ('mode', 1),\n",
              "  ('necessary', 1),\n",
              "  ('offer', 1),\n",
              "  ('online', 1),\n",
              "  ('orient', 1),\n",
              "  ('page', 1),\n",
              "  ('particularly', 2),\n",
              "  ('passionate', 1),\n",
              "  ('people', 1),\n",
              "  ('place', 1),\n",
              "  ('portal', 1),\n",
              "  ('potential', 1),\n",
              "  ('program', 1),\n",
              "  ('rate', 2),\n",
              "  ('retail', 1),\n",
              "  ('revolutionize', 1),\n",
              "  ('right', 1),\n",
              "  ('seed', 1),\n",
              "  ('skill', 1),\n",
              "  ('society', 1),\n",
              "  ('speak', 1),\n",
              "  ('start', 1),\n",
              "  ('technology', 1),\n",
              "  ('time', 1),\n",
              "  ('timer', 1),\n",
              "  ('tool', 1),\n",
              "  ('top', 1),\n",
              "  ('trading', 2),\n",
              "  ('truly', 1),\n",
              "  ('use', 1),\n",
              "  ('user', 2),\n",
              "  ('various', 1),\n",
              "  ('way', 1),\n",
              "  ('youngster', 1)]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWh7XtxXI9X4",
        "colab_type": "text"
      },
      "source": [
        "# **Building the Topic Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyqIThonI61p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build LDA model\n",
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
        "                                           id2word=id2word,\n",
        "                                           num_topics=20, \n",
        "                                           random_state=100,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=10,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sak8pbQFJf30",
        "colab_type": "text"
      },
      "source": [
        "# **View the topics in LDA model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efUGubqnJqC0",
        "colab_type": "text"
      },
      "source": [
        "The above LDA model is built with 20 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic.You can see the keywords for each topic and the weightage(importance) of each keyword using lda_model.print_topics() as shown next."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIA5vwUqJh3t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1472
        },
        "outputId": "6fd1f1f7-7d4b-4901-e75a-a424a19251d6"
      },
      "source": [
        "# Print the Keyword in the 10 topics\n",
        "pprint(lda_model.print_topics())\n",
        "doc_lda = lda_model[corpus]"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(0,\n",
            "  '0.037*\"resume\" + 0.029*\"human\" + 0.025*\"receive\" + 0.020*\"serve\" + '\n",
            "  '0.018*\"single\" + 0.017*\"lab\" + 0.013*\"chief\" + 0.012*\"mobility\" + '\n",
            "  '0.011*\"cell\" + 0.011*\"experiment\"'),\n",
            " (1,\n",
            "  '0.040*\"business\" + 0.030*\"management\" + 0.026*\"experience\" + 0.020*\"team\" + '\n",
            "  '0.017*\"client\" + 0.016*\"year\" + 0.016*\"service\" + 0.016*\"manage\" + '\n",
            "  '0.012*\"strategy\" + 0.011*\"work\"'),\n",
            " (2,\n",
            "  '0.028*\"base\" + 0.022*\"com\" + 0.020*\"child\" + 0.019*\"early\" + '\n",
            "  '0.018*\"technology\" + 0.015*\"learn\" + 0.014*\"science\" + 0.014*\"assessment\" + '\n",
            "  '0.014*\"interest\" + 0.014*\"http\"'),\n",
            " (3,\n",
            "  '0.019*\"recruitment\" + 0.016*\"design\" + 0.015*\"requirement\" + '\n",
            "  '0.014*\"different\" + 0.014*\"project\" + 0.014*\"work\" + 0.014*\"candidate\" + '\n",
            "  '0.013*\"team\" + 0.013*\"page\" + 0.013*\"time\"'),\n",
            " (4,\n",
            "  '0.036*\"work\" + 0.021*\"also\" + 0.019*\"policy\" + 0.019*\"role\" + '\n",
            "  '0.018*\"various\" + 0.017*\"government\" + 0.016*\"international\" + '\n",
            "  '0.016*\"include\" + 0.014*\"member\" + 0.013*\"part\"'),\n",
            " (5,\n",
            "  '0.067*\"technology\" + 0.033*\"engineering\" + 0.027*\"engineer\" + 0.027*\"work\" '\n",
            "  '+ 0.023*\"years_month\" + 0.019*\"student\" + 0.019*\"software\" + 0.017*\"madra\" '\n",
            "  '+ 0.015*\"tech\" + 0.015*\"pursue\"'),\n",
            " (6,\n",
            "  '0.038*\"datum\" + 0.034*\"design\" + 0.027*\"experience\" + 0.026*\"product\" + '\n",
            "  '0.023*\"tool\" + 0.020*\"use\" + 0.020*\"technology\" + 0.018*\"develop\" + '\n",
            "  '0.017*\"application\" + 0.016*\"automation\"'),\n",
            " (7,\n",
            "  '0.044*\"marketing\" + 0.022*\"brand\" + 0.019*\"sector\" + 0.016*\"family\" + '\n",
            "  '0.016*\"public\" + 0.015*\"health\" + 0.015*\"digital\" + 0.014*\"fund\" + '\n",
            "  '0.014*\"medium\" + 0.013*\"content\"'),\n",
            " (8,\n",
            "  '0.054*\"test\" + 0.049*\"testing\" + 0.022*\"script\" + 0.020*\"system\" + '\n",
            "  '0.019*\"use\" + 0.016*\"modeling\" + 0.016*\"create\" + 0.013*\"hadoop\" + '\n",
            "  '0.012*\"feature\" + 0.012*\"manual\"'),\n",
            " (9,\n",
            "  '0.025*\"food\" + 0.018*\"specialist\" + 0.016*\"sustainable\" + 0.016*\"woman\" + '\n",
            "  '0.014*\"country\" + 0.013*\"suitable\" + 0.013*\"explore\" + 0.013*\"community\" + '\n",
            "  '0.013*\"career\" + 0.013*\"novel\"'),\n",
            " (10,\n",
            "  '0.022*\"technology\" + 0.020*\"company\" + 0.019*\"lead\" + 0.015*\"global\" + '\n",
            "  '0.014*\"startup\" + 0.013*\"world\" + 0.012*\"help\" + 0.012*\"people\" + '\n",
            "  '0.011*\"leadership\" + 0.011*\"leader\"'),\n",
            " (11,\n",
            "  '0.048*\"employee\" + 0.039*\"hire\" + 0.032*\"interview\" + 0.028*\"job\" + '\n",
            "  '0.024*\"staffing\" + 0.018*\"process\" + 0.017*\"end\" + 0.017*\"position\" + '\n",
            "  '0.016*\"talent\" + 0.015*\"law\"'),\n",
            " (12,\n",
            "  '0.040*\"management\" + 0.028*\"professional\" + 0.026*\"strong\" + 0.026*\"work\" + '\n",
            "  '0.025*\"marketing\" + 0.024*\"focus\" + 0.023*\"experienced\" + 0.022*\"market\" + '\n",
            "  '0.020*\"program\" + 0.019*\"information\"'),\n",
            " (13,\n",
            "  '0.111*\"datum\" + 0.056*\"science\" + 0.039*\"analytic\" + 0.035*\"use\" + '\n",
            "  '0.030*\"analysis\" + 0.024*\"model\" + 0.023*\"data\" + 0.017*\"algorithm\" + '\n",
            "  '0.016*\"statistical\" + 0.015*\"optimization\"'),\n",
            " (14,\n",
            "  '0.066*\"sale\" + 0.038*\"month\" + 0.024*\"order\" + 0.021*\"increase\" + '\n",
            "  '0.017*\"region\" + 0.017*\"negotiation\" + 0.016*\"demonstrated_history\" + '\n",
            "  '0.014*\"channel\" + 0.013*\"letter\" + 0.012*\"transfer\"'),\n",
            " (15,\n",
            "  '0.044*\"product\" + 0.032*\"ai\" + 0.032*\"mentor\" + 0.024*\"analytic\" + '\n",
            "  '0.020*\"risk\" + 0.019*\"bank\" + 0.016*\"responsible\" + 0.016*\"enterprise\" + '\n",
            "  '0.016*\"serve\" + 0.016*\"practice\"'),\n",
            " (16,\n",
            "  '0.053*\"project\" + 0.020*\"screening\" + 0.017*\"plant\" + 0.016*\"full\" + '\n",
            "  '0.016*\"draft\" + 0.015*\"oracle\" + 0.014*\"coordinate\" + 0.012*\"cognizant\" + '\n",
            "  '0.011*\"type\" + 0.010*\"well_verse\"'),\n",
            " (17,\n",
            "  '0.055*\"research\" + 0.035*\"student\" + 0.017*\"review\" + 0.016*\"framework\" + '\n",
            "  '0.015*\"course\" + 0.014*\"technique\" + 0.014*\"system\" + 0.013*\"write\" + '\n",
            "  '0.012*\"work\" + 0.012*\"task\"'),\n",
            " (18,\n",
            "  '0.046*\"banking\" + 0.041*\"system\" + 0.016*\"study\" + 0.016*\"pipeline\" + '\n",
            "  '0.015*\"text\" + 0.012*\"show\" + 0.012*\"analysis\" + 0.011*\"fluid\" + '\n",
            "  '0.009*\"research\" + 0.008*\"flow\"'),\n",
            " (19,\n",
            "  '0.021*\"utility\" + 0.020*\"supervise\" + 0.019*\"catalyst\" + 0.014*\"foundation\" '\n",
            "  '+ 0.013*\"determination\" + 0.013*\"agenda\" + 0.013*\"batch\" + 0.011*\"ever\" + '\n",
            "  '0.011*\"subsidiary\" + 0.009*\"outstanding\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5lm_SN6rLSkV",
        "colab_type": "text"
      },
      "source": [
        "Topic 0 is represented as \n",
        "(0,\n",
        "  '0.037*\"resume\" + 0.029*\"human\" + 0.025*\"receive\" + 0.020*\"serve\" + '\n",
        "  '0.018*\"single\" + 0.017*\"lab\" + 0.013*\"chief\" + 0.012*\"mobility\" + '\n",
        "  '0.011*\"cell\" + 0.011*\"experiment\"'),\n",
        "It means the top 10 keywords that contribute to this topic are: resume,human etc and the weight of resume on Topic 0 is 0.037\n",
        "The weights reflect how important a keyword is to that topic.\n",
        "Looking at these keywords, we need to guess what this topic could be.\n",
        "Likewise we you go through the remaining topic keywords and judge what the topic is"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8THcx4hkMGrW",
        "colab_type": "text"
      },
      "source": [
        "# **Compute Model Perplexity and Coherence Score**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQNNEmA-MI8y",
        "colab_type": "text"
      },
      "source": [
        "Model perplexity and topic coherence provide a convenient measure to judge how good a given topic model is."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEjK7GmbKxai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "e4c0b6ed-0e4d-4bc4-d55e-05268053959f"
      },
      "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)\n",
        "#We have got a low coherence score. \n",
        "#We can try to adjust your parameters alpha = .1, beta = .01 or .001, seed = 123 or get better data"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Perplexity:  -7.652943371086192\n",
            "\n",
            "Coherence Score:  0.3947378205683595\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pHHXyDXCM8wS",
        "colab_type": "text"
      },
      "source": [
        "# **Visualize the topics-keywords**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jeHa9x-NHGi",
        "colab_type": "text"
      },
      "source": [
        "Now that the LDA model is built, the next step is to examine the produced topics and the associated keywords"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gA2finyM6Fx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1026
        },
        "outputId": "bf0c0035-c043-4d99-8ef6-8e0a514850b3"
      },
      "source": [
        "# Visualize the topics\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(lda_model, corpus, id2word)\n",
        "vis"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
            "of pandas will change to not sort by default.\n",
            "\n",
            "To accept the future behavior, pass 'sort=False'.\n",
            "\n",
            "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
            "\n",
            "  return pd.concat([default_term_info] + list(topic_dfs))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1211398114799066089918417234\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1211398114799066089918417234_data = {\"mdsDat\": {\"x\": [0.2948108061239236, 0.22864637286349163, 0.14402135339355518, 0.17514324803724746, 0.07397520869780072, 0.15339018746192196, 0.050094804209218274, -0.04020315984289578, 0.03127558712995354, -0.047763109065467615, -0.04905804313381318, -0.042355330261260454, -0.14731635728262246, -0.07877926848088408, -0.1349464278508317, -0.11780385935888744, -0.040143855363887294, -0.1363084970994551, -0.14708710473068268, -0.16959255544642482], \"y\": [0.030751463588664065, -0.08921739733192455, 0.17514015635045296, -0.20219991733320375, 0.09539277312489876, 0.07255427678939852, 0.18169395756355475, -0.10550126321211817, -0.23416940431350985, 0.07052084185434762, -0.011566266846083214, -0.030971538374348764, -0.054445190367912816, 0.09037582931739742, -0.01368915261132771, -0.07533923672859788, 0.018189691899729098, 0.04546859892497425, 0.017259418291091573, 0.01975235941451794], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 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\"part\", \"part\", \"partnership\", \"passion\", \"patient\", \"pay\", \"payroll\", \"pdlc\", \"peacekeeping\", \"people\", \"people\", \"people\", \"people\", \"perform\", \"perform\", \"perform\", \"perform\", \"performance\", \"performance\", \"performance\", \"performance\", \"period\", \"permanent\", \"phase\", \"phase\", \"phase\", \"phase\", \"phd\", \"philanthropy\", \"physics\", \"pioneer\", \"pipeline\", \"placement\", \"plan\", \"plan\", \"plan\", \"plan\", \"plant\", \"plant\", \"platform\", \"platform\", \"platform\", \"platform\", \"policy\", \"policy\", \"politic\", \"political\", \"polymer\", \"portfolio\", \"portfolio\", \"portfolio\", \"portfolio\", \"position\", \"position\", \"position\", \"post_graduate\", \"posting\", \"potential\", \"potential\", \"potential\", \"poverty\", \"power\", \"power\", \"power\", \"practice\", \"practice\", \"practice\", \"predict\", \"prediction\", \"premier\", \"prepare\", \"prepare\", \"prepare\", \"prepare\", \"present\", \"present\", \"presently\", \"previously\", \"price\", \"primarily\", \"principal\", \"print\", \"printer\", \"privacy\", \"private\", \"private_limite\", \"problem\", \"problem\", \"problem\", \"problem_solving\", \"process\", \"process\", \"process\", \"process\", \"process\", \"procure\", \"produce\", \"product\", \"product\", \"product\", \"product\", \"product\", \"product\", \"product\", \"production\", \"production\", \"production\", \"production\", \"professional\", \"professional\", \"professional\", \"professional\", \"professional\", \"professional\", \"professionally\", \"profitability\", \"profitability\", \"program\", \"program\", \"program\", \"program\", \"program\", \"programme\", \"programming\", \"project\", \"project\", \"project\", \"project\", \"project\", \"project\", \"project\", \"promotion\", \"proposal\", \"protein\", \"protein\", \"protein\", \"provide\", \"provide\", \"provide\", \"provide\", \"psingularity\", \"public\", \"public\", \"public\", \"publication\", \"publication\", \"purchase\", \"pursue\", \"pwc\", \"qlikview\", \"qualification\", \"quantum\", \"quantum\", \"query\", \"question\", \"quote\", \"range\", \"range\", \"rank\", \"rate\", \"rate\", \"rate\", \"rate\", \"rating\", \"reactor\", \"ready\", \"reality\", \"reality\", \"receive\", \"recently\", \"recognize\", \"recognize\", \"record\", \"recovery\", \"recruit\", \"recruit\", \"recruiter\", \"recruiting\", \"recruitment\", \"recruitment\", \"reference\", \"referral\", \"reform\", \"regard\", \"region\", \"region\", \"regression\", \"regression\", \"regulatory\", \"relaso\", \"relationship\", \"relationship\", \"release\", \"release\", \"relevant\", \"replication\", \"report\", \"report\", \"report\", \"reporting\", \"representative\", \"reputation\", \"require\", \"requirement\", \"requirement\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"researcher\", \"researcher\", \"resource\", \"resource\", \"respect\", \"responsible\", \"responsible\", \"responsible\", \"responsible\", \"result_oriented\", \"resume\", \"return\", \"review\", \"review\", \"reward\", \"rheology\", \"rich\", \"rinalytic\", \"risk\", \"risk\", \"robotic\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"root\", \"root\", \"route\", \"rule\", \"rural\", \"rural\", \"safety\", \"salary\", \"sale\", \"sale\", \"sale\", \"sale\", \"say\", \"scalable\", \"schedule\", \"schedule\", \"scheduling\", \"scheme\", \"scholar\", \"school\", \"school\", \"science\", \"science\", \"science\", \"science\", \"science\", \"science\", \"scientific\", \"scientist\", \"scientist\", \"scikit\", \"scorecard\", \"screening\", \"script\", \"scripting\", \"search\", \"second\", \"secondary\", \"section\", \"sector\", \"sector\", \"sector\", \"sector\", \"secure\", \"security\", \"seed\", \"selection\", \"selenium\", \"self\", \"self\", \"self\", \"sell\", \"sell\", \"sell\", \"serve\", \"serve\", \"server\", \"service\", \"service\", \"service\", \"set\", \"set\", \"several\", \"shipping\", \"show\", \"signal\", \"simulate\", \"single\", \"site\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skille\", \"skille\", \"slag\", \"sleep\", \"smart\", \"sme\", \"soap\", \"social\", \"social\", \"social\", \"society\", \"society\", \"software\", \"software\", \"software\", \"solar\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solve\", \"solve\", \"solve\", \"source\", \"source\", \"spark\", \"spark\", \"spark\", \"speaker\", \"special\", \"special\", \"specialist\", \"specialization\", \"specialtie\", \"specialtie\", \"specialtie\", \"specific\", \"specification\", \"speech\", \"speeche\", \"spend\", \"spirit\", \"sport\", \"spread\", \"spring\", \"spring\", \"sql\", \"stack\", \"staff\", \"staff\", \"staffing\", \"stage\", \"stage\", \"stake\", \"stakeholder\", \"standard\", \"start\", \"start\", \"start\", \"start\", \"startup\", \"state\", \"state\", \"statically\", \"statistic\", \"statistical\", \"statutory\", \"stock\", \"store\", \"storyline\", \"strategic\", \"strategic\", \"strategic\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"stray\", \"strengthen\", \"strong\", \"strong\", \"strong\", \"strong\", \"strong\", \"structural\", \"structural\", \"structure\", \"structure\", \"student\", \"student\", \"student\", \"study\", \"study\", \"study\", \"study\", \"subject\", \"submission\", \"subscription\", \"subsidiary\", \"success\", \"suitable\", \"supervise\", \"supply\", \"support\", \"support\", \"support\", \"support\", \"surface\", \"surface\", \"surge\", \"survey\", \"sustain\", \"sustainable\", \"sustainable\", \"sustainable\", \"svm\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"talent\", \"talent\", \"talent\", \"talent\", \"target\", \"target\", \"target\", \"task\", \"tax\", \"teach\", \"teach\", \"teacher\", \"team\", \"team\", \"team\", \"team\", \"team\", \"tech\", \"tech\", \"tech\", \"technical\", \"technical\", \"technical\", \"technique\", \"technique\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"telangana\", \"temporary\", \"test\", \"test\", \"test\", \"test\", \"testing\", \"testing\", \"testing\", \"text\", \"theoretical\", \"thesis\", \"think\", \"tie\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time_serie\", \"title\", \"today\", \"today\", \"tool\", \"tool\", \"tool\", \"top\", \"top\", \"top\", \"top\", \"top\", \"topic\", \"tracking\", \"trading\", \"traffic\", \"training\", \"training\", \"training\", \"training\", \"transfer\", \"transfer\", \"transformation\", \"transformation\", \"transformation\", \"transformation\", \"translational\", \"transportation\", \"travel\", \"tree\", \"try\", \"tube\", \"turn\", \"type\", \"type\", \"type\", \"unique\", \"university\", \"update\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"user\", \"user\", \"user\", \"user\", \"utility\", \"vaccine\", \"validate\", \"varied\", \"various\", \"various\", \"various\", \"various\", \"various\", \"various\", \"various\", \"various\", \"various\", \"vector\", \"vehicle\", \"vehicle\", \"vendor\", \"vendor\", \"versatile\", \"video\", \"view\", \"view\", \"village\", \"visit\", \"visualization\", \"visualization\", \"walk\", \"warehouse\", \"watch\", \"wave\", \"web\", \"web\", \"website\", \"welfare\", \"well_verse\", \"wide_range\", \"wide_range\", \"wipro\", \"wizsprint\", \"woman\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workflow\", \"workplace\", \"world\", \"world\", \"world\", \"worldwide\", \"would\", \"write\", \"write\", \"writer\", \"writer\", \"writer\", \"writing\", \"www\", \"yantrajaal\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"years_month\", \"yet\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [2, 4, 11, 7, 5, 13, 8, 18, 14, 12, 3, 6, 19, 15, 17, 9, 16, 10, 1, 20]};\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "1      0.294811  0.030751       1        1  25.394558\n",
              "3      0.228646 -0.089217       2        1  11.791391\n",
              "10     0.144021  0.175140       3        1   8.855008\n",
              "6      0.175143 -0.202200       4        1   6.788202\n",
              "4      0.073975  0.095393       5        1   6.432961\n",
              "12     0.153390  0.072554       6        1   6.221451\n",
              "7      0.050095  0.181694       7        1   4.627520\n",
              "17    -0.040203 -0.105501       8        1   3.843676\n",
              "13     0.031276 -0.234169       9        1   3.819776\n",
              "11    -0.047763  0.070521      10        1   2.773047\n",
              "2     -0.049058 -0.011566      11        1   2.614376\n",
              "5     -0.042355 -0.030972      12        1   2.533395\n",
              "18    -0.147316 -0.054445      13        1   2.273535\n",
              "14    -0.078779  0.090376      14        1   2.233558\n",
              "16    -0.134946 -0.013689      15        1   1.936957\n",
              "8     -0.117804 -0.075339      16        1   1.887772\n",
              "15    -0.040144  0.018190      17        1   1.879537\n",
              "9     -0.136308  0.045469      18        1   1.827064\n",
              "0     -0.147087  0.017259      19        1   1.483748\n",
              "19    -0.169593  0.019752      20        1   0.782471, topic_info=     Category        Freq           Term       Total  loglift  logprob\n",
              "17    Default  356.000000          datum  356.000000  30.0000  30.0000\n",
              "178   Default  516.000000     management  516.000000  29.0000  29.0000\n",
              "78    Default  343.000000     technology  343.000000  28.0000  28.0000\n",
              "337   Default  337.000000        project  337.000000  27.0000  27.0000\n",
              "433   Default  190.000000           sale  190.000000  26.0000  26.0000\n",
              "...       ...         ...            ...         ...      ...      ...\n",
              "1577  Topic20    2.667719     wide_range    5.884917   4.0593  -5.0006\n",
              "421   Topic20    3.193082  profitability    9.326271   3.7786  -4.8208\n",
              "2315  Topic20    3.060605           self   11.870920   3.4950  -4.8632\n",
              "4216  Topic20    2.528496           cold    8.483460   3.6400  -5.0542\n",
              "774   Topic20    2.729016    sustainable   23.481607   2.6982  -4.9779\n",
              "\n",
              "[1062 rows x 6 columns], token_table=      Topic      Freq         Term\n",
              "term                              \n",
              "563       1  0.981592      ability\n",
              "1383      5  0.910065     academic\n",
              "1383      8  0.079136     academic\n",
              "2617      3  0.941124   accelerate\n",
              "626      14  0.580279  achievement\n",
              "...     ...       ...          ...\n",
              "253       9  0.020074         year\n",
              "253      12  0.008603         year\n",
              "253      16  0.002868         year\n",
              "1127     12  0.963569  years_month\n",
              "3911     19  0.757067          yet\n",
              "\n",
              "[1671 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 4, 11, 7, 5, 13, 8, 18, 14, 12, 3, 6, 19, 15, 17, 9, 16, 10, 1, 20])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrvVr67jObdR",
        "colab_type": "text"
      },
      "source": [
        "# **Interpretation of this chart:**\n",
        "\n",
        "Each bubble on the left-hand side plot represents a topic. The larger the bubble, the more prevalent is that topic.\n",
        "A good topic model will have fairly big, non-overlapping bubbles scattered throughout the chart instead of being clustered in one quadrant.\n",
        "A model with too many topics, will typically have many overlaps, small sized bubbles clustered in one region of the chart.\n",
        "If you move the cursor over one of the bubbles, the words and bars on the right-hand side will update. \n",
        "These words are the salient keywords that form the selected topic.\n",
        "So far we have built a model by using 20 as the number of natural topics in the document.\n",
        "Now we will improve upon this model by using Mallet’s version of LDA algorithm and \n",
        "then we will focus on how to arrive at the optimal number of topics given any large corpus of text."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc0IFVPZP2Cw",
        "colab_type": "text"
      },
      "source": [
        "# **Building LDA Mallet Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wCUyqkHsQClg",
        "colab_type": "text"
      },
      "source": [
        "Mallet’s version, however, often gives a better quality of topics.\n",
        "Download File: http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXBi9xwMNLA-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "ed7d615c-ab0b-4b1c-cc4a-09dd0c7781d0"
      },
      "source": [
        "!wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-03-19 14:46:03--  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... 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  17.2MB/s    in 0.9s    \n",
            "\n",
            "2020-03-19 14:46:04 (17.2 MB/s) - ‘mallet-2.0.8.zip’ saved [16184794/16184794]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rMvpmn9VModx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip /content/mallet-2.0.8.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgDOKNdHQsfV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#/content/mallet-2.0.8/bin/mallet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZgjifPwSg3S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aed0a46e-6b73-4550-a782-360a65f46ade"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASPS6acjRCmx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "79278a08-d47e-4ba3-dc58-30b51ac4136f"
      },
      "source": [
        "mallet_path = '/content/mallet-2.0.8/bin/mallet' # update this path\n",
        "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=20, id2word=id2word)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:402: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITxOUwTQS4Yx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2054
        },
        "outputId": "3a8c21c3-dbe1-4cbc-cf4e-cdc5373f722d"
      },
      "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)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(5,\n",
            "  [('program', 0.06776003452740613),\n",
            "   ('training', 0.029779887785930083),\n",
            "   ('development', 0.02762192490289167),\n",
            "   ('support', 0.022442813983599482),\n",
            "   ('service', 0.020716443677168753),\n",
            "   ('impact', 0.01985325852395339),\n",
            "   ('develop', 0.017695295640914977),\n",
            "   ('human', 0.01640051791109193),\n",
            "   ('community', 0.015105740181268883),\n",
            "   ('include', 0.0146741476046612)]),\n",
            " (10,\n",
            "  [('team', 0.08649062380405664),\n",
            "   ('provide', 0.03673938002296211),\n",
            "   ('drive', 0.03176425564485266),\n",
            "   ('performance', 0.024492920015308076),\n",
            "   ('process', 0.022196708763872943),\n",
            "   ('set', 0.02104860313815538),\n",
            "   ('page', 0.02066590126291619),\n",
            "   ('service', 0.020283199387677),\n",
            "   ('ensure', 0.019135093761959432),\n",
            "   ('support', 0.018752391886720246)]),\n",
            " (15,\n",
            "  [('industry', 0.04858757062146893),\n",
            "   ('client', 0.045574387947269306),\n",
            "   ('solution', 0.03465160075329567),\n",
            "   ('leadership', 0.03389830508474576),\n",
            "   ('business', 0.03352165725047081),\n",
            "   ('deliver', 0.026365348399246705),\n",
            "   ('organization', 0.023728813559322035),\n",
            "   ('level', 0.022222222222222223),\n",
            "   ('people', 0.022222222222222223),\n",
            "   ('consulting', 0.021468926553672316)]),\n",
            " (14,\n",
            "  [('financial', 0.04054613156805958),\n",
            "   ('business', 0.03640877120397187),\n",
            "   ('finance', 0.033512618949110465),\n",
            "   ('corporate', 0.031857674803475386),\n",
            "   ('investment', 0.02772031443938767),\n",
            "   ('company', 0.021928009929664875),\n",
            "   ('service', 0.019445593711212246),\n",
            "   ('office', 0.016549441456350848),\n",
            "   ('page', 0.016135705419942078),\n",
            "   ('bank', 0.014894497310715763)]),\n",
            " (4,\n",
            "  [('work', 0.09634551495016612),\n",
            "   ('create', 0.041765543426673),\n",
            "   ('communication', 0.02800189843379212),\n",
            "   ('role', 0.026578073089700997),\n",
            "   ('campaign', 0.023730422401518746),\n",
            "   ('experience', 0.023255813953488372),\n",
            "   ('online', 0.018984337921214997),\n",
            "   ('part', 0.017560512577123873),\n",
            "   ('passionate', 0.01613668723303275),\n",
            "   ('great', 0.015187470336971999)]),\n",
            " (3,\n",
            "  [('student', 0.0611587982832618),\n",
            "   ('year', 0.03272532188841202),\n",
            "   ('school', 0.028433476394849784),\n",
            "   ('write', 0.02467811158798283),\n",
            "   ('give', 0.022532188841201718),\n",
            "   ('content', 0.02145922746781116),\n",
            "   ('graduate', 0.017167381974248927),\n",
            "   ('young', 0.016630901287553648),\n",
            "   ('event', 0.01555793991416309),\n",
            "   ('interest', 0.014484978540772532)]),\n",
            " (11,\n",
            "  [('experience', 0.05582615211689772),\n",
            "   ('test', 0.034095166729112024),\n",
            "   ('tool', 0.0258523791682278),\n",
            "   ('year', 0.023604346197077557),\n",
            "   ('testing', 0.023229674035219184),\n",
            "   ('application', 0.020606968902210566),\n",
            "   ('knowledge', 0.019482952416635443),\n",
            "   ('report', 0.018733608092918696),\n",
            "   ('requirement', 0.018358935931060322),\n",
            "   ('involve', 0.01798426376920195)]),\n",
            " (7,\n",
            "  [('project', 0.04441327723235157),\n",
            "   ('design', 0.043478260869565216),\n",
            "   ('material', 0.02758298270219729),\n",
            "   ('study', 0.022907900888265546),\n",
            "   ('engineering', 0.020102851799906497),\n",
            "   ('flow', 0.015427769985974754),\n",
            "   ('analysis', 0.014025245441795231),\n",
            "   ('field', 0.013557737260402058),\n",
            "   ('chemical', 0.013090229079008883),\n",
            "   ('water', 0.013090229079008883)]),\n",
            " (16,\n",
            "  [('work', 0.06822344322344322),\n",
            "   ('base', 0.04487179487179487),\n",
            "   ('system', 0.039377289377289376),\n",
            "   ('order', 0.022893772893772892),\n",
            "   ('cost', 0.021062271062271064),\n",
            "   ('control', 0.019230769230769232),\n",
            "   ('part', 0.01694139194139194),\n",
            "   ('structure', 0.016483516483516484),\n",
            "   ('group', 0.015567765567765568),\n",
            "   ('document', 0.015567765567765568)]),\n",
            " (8,\n",
            "  [('skill', 0.0782934833567745),\n",
            "   ('work', 0.04922644163150492),\n",
            "   ('learn', 0.04313173933427098),\n",
            "   ('good', 0.0379746835443038),\n",
            "   ('year', 0.033286451007969994),\n",
            "   ('building', 0.027660571964369433),\n",
            "   ('expertise', 0.02625410220346929),\n",
            "   ('challenge', 0.02531645569620253),\n",
            "   ('base', 0.02344116268166901),\n",
            "   ('solution', 0.02297233942803563)])]\n",
            "\n",
            "Coherence Score:  0.41380829686079235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dk0hJNwxTMJP",
        "colab_type": "text"
      },
      "source": [
        "By changing the LDA algorithm, we increased the coherence score from 0.3 to 0.4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9APp-a3TSCB",
        "colab_type": "text"
      },
      "source": [
        "# **How to find the optimal number of topics for LDA?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WiN95_rMThFu",
        "colab_type": "text"
      },
      "source": [
        "Build many LDA models with different values of number of topics (k) and pick the one that gives the highest coherence value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5e1gAgT_ToQy",
        "colab_type": "text"
      },
      "source": [
        "The compute_coherence_values() function trains multiple LDA models and provides the models and their corresponding coherence scores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTk7_eXLTrxk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbxmJK-aT_Ve",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyYsNE-QUKqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "1bc15907-983c-46e3-d173-9723152c0c34"
      },
      "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()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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1Bm6o64W9k/CeB66vZZ/eeO4uflLT66r6KvAqeLqY6hqTUyorlfkbCjirZ6uQ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\nIIwxxtTIEoQxxpga/X+BHPMbN9h/QwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01PnIrX6Vvmv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "b7db9cbf-e65f-4f17-9e1c-06a284c12fa8"
      },
      "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))"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num Topics = 2  has Coherence Value of 0.3792\n",
            "Num Topics = 8  has Coherence Value of 0.4406\n",
            "Num Topics = 14  has Coherence Value of 0.4255\n",
            "Num Topics = 20  has Coherence Value of 0.4437\n",
            "Num Topics = 26  has Coherence Value of 0.4151\n",
            "Num Topics = 32  has Coherence Value of 0.4255\n",
            "Num Topics = 38  has Coherence Value of 0.4198\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3vF08BQV2kp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "outputId": "de99d7c0-9bdd-41ff-b5db-db2e34212c4e"
      },
      "source": [
        "# Select the model and print the topics\n",
        "optimal_model = model_list[1]\n",
        "model_topics = optimal_model.show_topics(formatted=False)\n",
        "pprint(optimal_model.print_topics(num_words=10))"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(0,\n",
            "  '0.054*\"project\" + 0.044*\"work\" + 0.029*\"experience\" + 0.023*\"design\" + '\n",
            "  '0.018*\"development\" + 0.017*\"software\" + 0.014*\"system\" + 0.014*\"test\" + '\n",
            "  '0.014*\"involve\" + 0.013*\"team\"'),\n",
            " (1,\n",
            "  '0.031*\"client\" + 0.024*\"management\" + 0.018*\"process\" + 0.017*\"end\" + '\n",
            "  '0.017*\"professional\" + 0.016*\"level\" + 0.014*\"handle\" + 0.013*\"source\" + '\n",
            "  '0.013*\"page\" + 0.013*\"team\"'),\n",
            " (2,\n",
            "  '0.048*\"research\" + 0.041*\"technology\" + 0.022*\"student\" + 0.018*\"work\" + '\n",
            "  '0.018*\"design\" + 0.017*\"engineering\" + 0.015*\"experienced\" + 0.014*\"strong\" '\n",
            "  '+ 0.014*\"system\" + 0.012*\"interest\"'),\n",
            " (3,\n",
            "  '0.039*\"business\" + 0.035*\"lead\" + 0.026*\"service\" + 0.020*\"team\" + '\n",
            "  '0.019*\"corporate\" + 0.019*\"strategy\" + 0.018*\"industry\" + '\n",
            "  '0.017*\"leadership\" + 0.016*\"global\" + 0.016*\"people\"'),\n",
            " (4,\n",
            "  '0.063*\"datum\" + 0.027*\"analytic\" + 0.025*\"model\" + 0.023*\"skill\" + '\n",
            "  '0.021*\"science\" + 0.018*\"experience\" + 0.017*\"domain\" + 0.016*\"customer\" + '\n",
            "  '0.016*\"solution\" + 0.015*\"data\"'),\n",
            " (5,\n",
            "  '0.059*\"year\" + 0.029*\"company\" + 0.018*\"financial\" + 0.018*\"experience\" + '\n",
            "  '0.016*\"work\" + 0.016*\"building\" + 0.016*\"large\" + 0.013*\"focus\" + '\n",
            "  '0.013*\"sector\" + 0.013*\"technology\"'),\n",
            " (6,\n",
            "  '0.057*\"management\" + 0.048*\"business\" + 0.040*\"marketing\" + 0.039*\"product\" '\n",
            "  '+ 0.034*\"sale\" + 0.028*\"market\" + 0.023*\"development\" + 0.022*\"operation\" + '\n",
            "  '0.020*\"strategy\" + 0.017*\"manage\"'),\n",
            " (7,\n",
            "  '0.034*\"work\" + 0.022*\"learn\" + 0.014*\"base\" + 0.014*\"include\" + '\n",
            "  '0.013*\"government\" + 0.013*\"world\" + 0.012*\"impact\" + 0.012*\"program\" + '\n",
            "  '0.011*\"challenge\" + 0.011*\"policy\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnEifz3_XLMy",
        "colab_type": "text"
      },
      "source": [
        "# **Finding the dominant topic in each sentence**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEKfIdssXNNx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "b129a3e0-7e90-4b24-dd78-b3a5d96f2d22"
      },
      "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)"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:402: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
            "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Document_No</th>\n",
              "      <th>Dominant_Topic</th>\n",
              "      <th>Topic_Perc_Contrib</th>\n",
              "      <th>Keywords</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.2334</td>\n",
              "      <td>year, company, financial, experience, work, bu...</td>\n",
              "      <td>nan    Keventer Agro    nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.1467</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>Top Skills    Entrepreneurship    Seed Capit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.3918</td>\n",
              "      <td>business, lead, service, team, corporate, stra...</td>\n",
              "      <td>nan    Responsible for managing Hughes Systiqu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.1437</td>\n",
              "      <td>client, management, process, end, professional...</td>\n",
              "      <td>Yashwant Mahadik  Yash  a seasoned global HR...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.2461</td>\n",
              "      <td>work, learn, base, include, government, world,...</td>\n",
              "      <td>RESUME         Techdata Solutions   India</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.4157</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>Top Skills    Business Development    Start ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.2710</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>A senior professional holding P L accountabi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.1593</td>\n",
              "      <td>datum, analytic, model, skill, science, experi...</td>\n",
              "      <td>An award winning Marketing Professional  Poo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.1541</td>\n",
              "      <td>work, learn, base, include, government, world,...</td>\n",
              "      <td>Top Skills    JavaScript    AJAX    Algorith...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.3185</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>A school dropout to an IIM graduate  my teme...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Document_No  ...                                               Text\n",
              "0            0  ...                        nan    Keventer Agro    nan\n",
              "1            1  ...    Top Skills    Entrepreneurship    Seed Capit...\n",
              "2            2  ...  nan    Responsible for managing Hughes Systiqu...\n",
              "3            3  ...    Yashwant Mahadik  Yash  a seasoned global HR...\n",
              "4            4  ...        RESUME         Techdata Solutions   India  \n",
              "5            5  ...    Top Skills    Business Development    Start ...\n",
              "6            6  ...    A senior professional holding P L accountabi...\n",
              "7            7  ...    An award winning Marketing Professional  Poo...\n",
              "8            8  ...    Top Skills    JavaScript    AJAX    Algorith...\n",
              "9            9  ...    A school dropout to an IIM graduate  my teme...\n",
              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2ERQyrjXrzE",
        "colab_type": "text"
      },
      "source": [
        "# **Find the most representative document for each topic**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLHBpaO2XGp7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "690d7fce-ebae-46d9-db41-d4d16768130c"
      },
      "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(10)"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic_Num</th>\n",
              "      <th>Topic_Perc_Contrib</th>\n",
              "      <th>Keywords</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7680</td>\n",
              "      <td>project, work, experience, design, development...</td>\n",
              "      <td>nan    NoBroker com is world s largest C2C rea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7596</td>\n",
              "      <td>client, management, process, end, professional...</td>\n",
              "      <td>A designer and marketer who looks for the in...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5910</td>\n",
              "      <td>research, technology, student, work, design, e...</td>\n",
              "      <td>I am a highly accomplished Technical leader ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.5860</td>\n",
              "      <td>business, lead, service, team, corporate, stra...</td>\n",
              "      <td>A seasoned leader in Corporate Strategy and ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.4872</td>\n",
              "      <td>datum, analytic, model, skill, science, experi...</td>\n",
              "      <td>nan    Aviation Services East Africa      The ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.4967</td>\n",
              "      <td>year, company, financial, experience, work, bu...</td>\n",
              "      <td>Multi   dimensional exposure with acumen for...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.5512</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>Specialties  With about 25 years of experien...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.4766</td>\n",
              "      <td>work, learn, base, include, government, world,...</td>\n",
              "      <td>Professionally and academically trained proj...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Topic_Num  ...                                               Text\n",
              "0        0.0  ...  nan    NoBroker com is world s largest C2C rea...\n",
              "1        1.0  ...    A designer and marketer who looks for the in...\n",
              "2        2.0  ...    I am a highly accomplished Technical leader ...\n",
              "3        3.0  ...    A seasoned leader in Corporate Strategy and ...\n",
              "4        4.0  ...  nan    Aviation Services East Africa      The ...\n",
              "5        5.0  ...    Multi   dimensional exposure with acumen for...\n",
              "6        6.0  ...    Specialties  With about 25 years of experien...\n",
              "7        7.0  ...    Professionally and academically trained proj...\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2kp6MaviZkHh",
        "colab_type": "text"
      },
      "source": [
        "The tabular output above actually has 7 rows, one each for a topic. It has the topic number, the keywords, and the most representative document. The Perc_Contribution column is nothing but the percentage contribution of the topic in the given document"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeRlGCLQbCQ6",
        "colab_type": "text"
      },
      "source": [
        "# **Topic distribution across documents**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65-1Yd6MbPW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "3eb54a88-fdff-44df-ffba-c89d1025b5d5"
      },
      "source": [
        "# Number of Documents for Each Topic\n",
        "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
        "topic_counts"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0    133\n",
              "4.0     99\n",
              "6.0     83\n",
              "5.0     81\n",
              "7.0     78\n",
              "0.0     67\n",
              "3.0     67\n",
              "1.0     52\n",
              "Name: Dominant_Topic, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPuONIFJbpud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "8b2f3a11-b1b5-4402-fbda-30039857fb14"
      },
      "source": [
        "\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 = sent_topics_sorteddf_mallet[['Topic_Num', '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"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dominant_Topic</th>\n",
              "      <th>Topic_Keywords</th>\n",
              "      <th>Num_Documents</th>\n",
              "      <th>Perc_Documents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>project, work, experience, design, development...</td>\n",
              "      <td>67</td>\n",
              "      <td>0.1015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>client, management, process, end, professional...</td>\n",
              "      <td>52</td>\n",
              "      <td>0.0788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>research, technology, student, work, design, e...</td>\n",
              "      <td>133</td>\n",
              "      <td>0.2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>business, lead, service, team, corporate, stra...</td>\n",
              "      <td>67</td>\n",
              "      <td>0.1015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>4.0</td>\n",
              "      <td>datum, analytic, model, skill, science, experi...</td>\n",
              "      <td>99</td>\n",
              "      <td>0.1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>5.0</td>\n",
              "      <td>year, company, financial, experience, work, bu...</td>\n",
              "      <td>81</td>\n",
              "      <td>0.1227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>6.0</td>\n",
              "      <td>management, business, marketing, product, sale...</td>\n",
              "      <td>83</td>\n",
              "      <td>0.1258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>work, learn, base, include, government, world,...</td>\n",
              "      <td>78</td>\n",
              "      <td>0.1182</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dominant_Topic  ... Perc_Documents\n",
              "0.0             0.0  ...         0.1015\n",
              "1.0             1.0  ...         0.0788\n",
              "2.0             2.0  ...         0.2015\n",
              "3.0             3.0  ...         0.1015\n",
              "4.0             4.0  ...         0.1500\n",
              "5.0             5.0  ...         0.1227\n",
              "6.0             6.0  ...         0.1258\n",
              "7.0             7.0  ...         0.1182\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    }
  ]
}