BERT-Topic-Modelling.ipynb

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  "metadata": {
    "colab": {
      "name": "BERT_Topic_Modelling.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXHLDxJdRzBi"
      },
      "source": [
        "# **BERTopic - Tutorial**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNa-KtKDRnus",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4273ada4-aadb-4c0a-ddb3-4059f412e2bd"
      },
      "source": [
        "!pip install bertopic[visualization] --quiet"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.7MB 11.3MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 307kB 56.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 71kB 12.1MB/s \n",
            "\u001b[K     |████████████████████████████████| 13.2MB 48.5MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.5MB 55.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 52.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.9MB 51.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 55.6MB/s \n",
            "\u001b[?25h  Building wheel for hdbscan (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bm-iSjr9z6dF"
      },
      "source": [
        "# **Imports**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd0uHueSz795"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "from copy import deepcopy\r\n",
        "from bertopic import BERTopic"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3VGFZ1USMTu"
      },
      "source": [
        "# **Load data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1KDDUurzpgz"
      },
      "source": [
        "df = pd.read_csv(\"data.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "ITGyQkMQzrma",
        "outputId": "108d2ac2-507e-4797-cfe2-f30b052fba8a"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Reviews</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>Saw the car on the road today and i found it t...</td>\n",
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              "      <td>Built quality is very solid and a quick peppy ...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Even it's base model has all features and safe...</td>\n",
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              "                                             Reviews\n",
              "0  Saw the car on the road today and i found it t...\n",
              "1  Built quality is very solid and a quick peppy ...\n",
              "2  Even it's base model has all features and safe..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_JdwlALzx46"
      },
      "source": [
        "docs = list(df.loc[:, \"Reviews\"].values)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quJJRrNg0Z_g",
        "outputId": "9e4f7a74-6fce-4b06-c78d-28b9e2d75218"
      },
      "source": [
        "docs[:5]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Saw the car on the road today and i found it to have good enough road presence.',\n",
              " 'Built quality is very solid and a quick peppy engine.',\n",
              " \"Even it's base model has all features and safety like traction control abs ebd etc\",\n",
              " 'The diesel engine is providing extreme performance to the car like a beast.',\n",
              " \"It's very comfortable and doesn't make us tired at all.\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBcNmZJzSTY8"
      },
      "source": [
        "# **Creating Topics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfhfzqkoSJ1I"
      },
      "source": [
        "model = BERTopic(language=\"english\")"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqdMzhTgqKfL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b81ff88d-cb49-487b-9f6d-762332a77252"
      },
      "source": [
        "topics, probs = model.fit_transform(docs)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 245M/245M [00:10<00:00, 23.1MB/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ua80usww-rj"
      },
      "source": [
        "# We can then extract most frequent topics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNptKBzHSbyS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "c73f855d-d1be-4aa3-a30b-441a98ceaea9"
      },
      "source": [
        "model.get_topic_freq()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>3</td>\n",
              "      <td>152</td>\n",
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              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "      <td>139</td>\n",
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              "      <th>3</th>\n",
              "      <td>10</td>\n",
              "      <td>60</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>39</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
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              "      <td>27</td>\n",
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              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
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              "      <th>10</th>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>9</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>11</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "    Topic  Count\n",
              "0      -1    154\n",
              "1       3    152\n",
              "2      14    139\n",
              "3      10     60\n",
              "4       8     39\n",
              "5       2     33\n",
              "6       0     30\n",
              "7       7     27\n",
              "8      12     27\n",
              "9       1     21\n",
              "10      6     20\n",
              "11      9     18\n",
              "12      4     16\n",
              "13      5     16\n",
              "14     13     15\n",
              "15     11     14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zMjoIyB0pd9"
      },
      "source": [
        "# Get Individual Topics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPrGs6ys0mqt",
        "outputId": "dfba30b9-c622-4d7b-ffd0-b80938c79e3b"
      },
      "source": [
        "model.get_topic(0)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('sound', 0.20647518388927114),\n",
              " ('lighting', 0.05904045659014696),\n",
              " ('bose', 0.052776100755048946),\n",
              " ('lights', 0.03936030439343131),\n",
              " ('awesome', 0.036118521776356785),\n",
              " ('good', 0.035078170500737096),\n",
              " ('amazing', 0.033548658623619845),\n",
              " ('great', 0.03219834532747863),\n",
              " ('gimmicky', 0.024852892350148082),\n",
              " ('adaptive', 0.024852892350148082)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCw_q8I7Sb03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17b900aa-cc5e-496b-8917-d9ea78f64b8b"
      },
      "source": [
        "model.get_topic(2)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('cost', 0.12703704731543825),\n",
              " ('maintenance', 0.10057315391189506),\n",
              " ('service', 0.07040120773832655),\n",
              " ('costs', 0.06814275683211399),\n",
              " ('little', 0.057560357128908717),\n",
              " ('low', 0.04022926156475803),\n",
              " ('less', 0.032539541046116914),\n",
              " ('costlier', 0.03036859017445161),\n",
              " ('goodwill', 0.03036859017445161),\n",
              " ('servicing', 0.028305154509779017)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6u335JDzDSr",
        "outputId": "669a0748-7e07-4308-9c54-7af809026444"
      },
      "source": [
        "model.get_topic(14)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('car', 0.08357972680098304),\n",
              " ('good', 0.04357671366284399),\n",
              " ('safety', 0.04106929322713067),\n",
              " ('suv', 0.03988365816260247),\n",
              " ('drive', 0.03256642096270172),\n",
              " ('excellent', 0.0309635676225596),\n",
              " ('comfortable', 0.02290996022730286),\n",
              " ('amazing', 0.022227541436662838),\n",
              " ('airbags', 0.021626624133744835),\n",
              " ('nice', 0.019017652727957822)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8c8LenB8Zyl"
      },
      "source": [
        "# **Visualize Topics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQKBcla28bY0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "outputId": "2ba1ec8f-cda6-44f1-ab4e-4448ac876b90"
      },
      "source": [
        "model.visualize_topics()"
      ],
      "execution_count": 12,
      "outputs": [
        {
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