LDA_Gensim.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install langdetect"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBtBPyHjV7P5",
        "outputId": "22d83942-0d1e-46e1-9fbe-808083c4b20d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langdetect\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from langdetect) (1.16.0)\n",
            "Building wheels for collected packages: langdetect\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=e083a4479565d8625b631a129417fd4f9a9c0a7ce4d05ca6cb8f9bf0d03838a5\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "Successfully built langdetect\n",
            "Installing collected packages: langdetect\n",
            "Successfully installed langdetect-1.0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download en"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJMrzh1FzSN9",
        "outputId": "acda6925-8ad4-4c9b-e532-d740362600c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;3m⚠ As of spaCy v3.0, shortcuts like 'en' are deprecated. Please use the\n",
            "full pipeline package name 'en_core_web_sm' instead.\u001b[0m\n",
            "Collecting en-core-web-sm==3.7.1\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.2 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.7.1) (3.7.4)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.2.3)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.3.4)\n",
            "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.9.4)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (6.4.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.66.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.6.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (24.0)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.3.0)\n",
            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (1.25.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.6.0)\n",
            "Requirement already satisfied: pydantic-core==2.16.3 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.16.3)\n",
            "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2024.2.2)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.7.11)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.3.0,>=8.2.2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.1.4)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.10.0,>=0.3.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (8.1.7)\n",
            "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.4.0,>=0.1.0->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (0.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.2->en-core-web-sm==3.7.1) (2.1.5)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfKsxnhjuwHL",
        "outputId": "71c623c5-1039-4d3b-d65e-ee2f29fd6ff0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import gensim\n",
        "import pandas as pd\n",
        "from langdetect import detect, LangDetectException\n",
        "import gensim\n",
        "import gensim.corpora as corpora\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.models import CoherenceModel\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from gensim.models import TfidfModel"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df = pd.read_csv('preprocess_final.csv')"
      ],
      "metadata": {
        "id": "ZwzhLIzzuxHA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df[\"select_tokens\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhc3PSOivHPg",
        "outputId": "d73db791-ed7c-4179-b055-d748eb7cf01f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      ['audience', 'cheering', 'applauding', 'hello'...\n",
              "1      ['im', 'funnier', 'told', 'introduce', 'heres'...\n",
              "2      ['netflix', 'standup', 'comedy', 'special', 't...\n",
              "3      ['kevin', 'bridges', 'overdue', 'catchup', 'co...\n",
              "4      ['get', 'knees', 'jacqueline', 'novak', 'trans...\n",
              "                             ...                        \n",
              "468    ['jammin', 'new', 'york', 'george', 'carlins',...\n",
              "469    ['australian', 'comedian', 'jim', 'jefferies',...\n",
              "470    ['hello', 'im', 'thomas', 'im', 'glad', 'meet'...\n",
              "471    ['complaints', 'grievances', 'hbo', 'standup',...\n",
              "472    ['full', 'transcript', 'bad', 'ya', 'final', '...\n",
              "Name: select_tokens, Length: 473, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTWsHjdzv78v",
        "outputId": "beb87fd6-d29a-46fa-f2d3-e79bf071b2e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(473, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_language(text):\n",
        "    try:\n",
        "        return detect(text)\n",
        "    except LangDetectException:\n",
        "        return None\n",
        "\n",
        "\n",
        "lda_df['language'] = lda_df['transcript'].apply(detect_language)\n"
      ],
      "metadata": {
        "id": "Rj1w0EnFvvlf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwx7AscrwADF",
        "outputId": "34db2270-7a77-4360-c42b-439a7058b0b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(473, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df[\"language\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Xolw8wbwUnq",
        "outputId": "d594d433-1c50-41da-b878-dd0596435caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language\n",
              "en    464\n",
              "it      7\n",
              "es      2\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "non_english_indices = lda_df[lda_df['language'] != 'en'].index.tolist()"
      ],
      "metadata": {
        "id": "eLX-0CPWwZ4K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df.drop(non_english_indices, inplace=True)"
      ],
      "metadata": {
        "id": "-TCHQ1_KwdC5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYNmoFkDwroS",
        "outputId": "dcf00140-f778-4d39-b04b-6d2c3be22af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(464, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(lda_df[\"select_tokens\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D689NJngwsez",
        "outputId": "c95b416c-f28d-470d-c384-6097b130c926"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "464"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(lda_df[\"select_tokens\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hqlnq4Vzw53Q",
        "outputId": "141ef606-9dc4-474d-af99-88660d9e2720"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval"
      ],
      "metadata": {
        "id": "z4uhbhgrxNBc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_df['select_tokens_lda'] = lda_df['select_tokens'].apply(literal_eval)"
      ],
      "metadata": {
        "id": "R7QlQSJ1xUp6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(lda_df[\"select_tokens_lda\"][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDJVZdSJxXIq",
        "outputId": "7052ac92-0e48-4067-e763-c59f88887265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lda_df[\"select_tokens_lda\"][0]"
      ],
      "metadata": {
        "id": "F-LXjswZxhRW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_words = lda_df[\"select_tokens_lda\"].tolist()"
      ],
      "metadata": {
        "id": "6GzHlJTHxn_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D34Sn5RAxy9X",
        "outputId": "05ca1682-f496-40f8-9af2-f7580c38a329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "464"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\n",
        "#trigram = gensim.models.Phrases(bigram[data_words], threshold=100)"
      ],
      "metadata": {
        "id": "WhLRvFWgxzzx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
        "#trigram_mod = gensim.models.phrases.Phraser(trigram)#results not great"
      ],
      "metadata": {
        "id": "CJUbcK8WycI5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(trigram_mod[bigram_mod[data_words[2]]])"
      ],
      "metadata": {
        "id": "KPEGucxByhPF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = stopwords.words('english')"
      ],
      "metadata": {
        "id": "EI3u6vrAzDAz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy"
      ],
      "metadata": {
        "id": "BuMTeHVA0TX6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy validate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjuC27_TW6xF",
        "outputId": "4a444a29-7058-41d7-d308-3a9f33531e4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r⠙ Loading compatibility table...\r⠹ Loading compatibility table...\r\u001b[2K\u001b[38;5;2m✔ Loaded compatibility table\u001b[0m\n",
            "\u001b[1m\n",
            "================= Installed pipeline packages (spaCy v3.7.4) =================\u001b[0m\n",
            "\u001b[38;5;4mℹ spaCy installation: /usr/local/lib/python3.10/dist-packages/spacy\u001b[0m\n",
            "\n",
            "NAME             SPACY            VERSION                            \n",
            "en_core_web_sm   >=3.7.2,<3.8.0   \u001b[38;5;2m3.7.1\u001b[0m   \u001b[38;5;2m✔\u001b[0m\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load('en_core_web_sm')"
      ],
      "metadata": {
        "id": "mM8UZoFj0NEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(nlp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "N7nFvh5cXHUd",
        "outputId": "49c16649-7840-4c46-fab0-71dc08094e25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "spacy.lang.en.English"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>spacy.lang.en.English</b><br/>def __call__(text: Union[str, Doc], *, disable: Iterable[str]=SimpleFrozenList(), component_cfg: Optional[Dict[str, Dict[str, Any]]]=None) -&gt; Doc</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/spacy/lang/en/__init__.py</a>A text-processing pipeline. Usually you&#x27;ll load this once per process,\n",
              "and pass the instance around your application.\n",
              "\n",
              "Defaults (class): Settings, data and factory methods for creating the `nlp`\n",
              "    object and processing pipeline.\n",
              "lang (str): IETF language code, such as &#x27;en&#x27;.\n",
              "\n",
              "DOCS: https://spacy.io/api/language</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
              "          element.onclick = (event) => {\n",
              "            event.preventDefault();\n",
              "            event.stopPropagation();\n",
              "            google.colab.files.view(element.textContent, 22);\n",
              "          };\n",
              "        }\n",
              "      }\n",
              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
              "        element.onclick = (event) => {\n",
              "          event.preventDefault();\n",
              "          event.stopPropagation();\n",
              "          element.classList.toggle('function-repr-contents-collapsed');\n",
              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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']):\n",
        "    texts_out = []\n",
        "    for sent in texts:\n",
        "        doc = nlp(\" \".join(sent))\n",
        "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
        "    return texts_out"
      ],
      "metadata": {
        "id": "ASQ0em5Cyk2I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_words_nostops = remove_stopwords(data_words)"
      ],
      "metadata": {
        "id": "Rjk7ikzt0euR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data_words_nostops)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2p-CR9df0nuC",
        "outputId": "f41c2eee-5c17-471e-9071-26a0542c0895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "464"
            ]
          },
          "metadata": {},
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data_words_bigrams = make_bigrams(data_words_nostops)"
      ],
      "metadata": {
        "id": "G5AiE1H40sNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_lemmatized = lemmatization(data_words_nostops, allowed_postags=['NOUN'])"
      ],
      "metadata": {
        "id": "jHELpl_p0uAl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "custom_stop_words = [\"audience\", \"shit\", \"fuck\", \"motherfucker\", \"time\",\n",
        "                     \"bitch\", \"thing\", \"word\", \"lot\", \"way\", \"crowd\", \"laugh\",\n",
        "                     \"chuckle\", \"laughing\", \"kev\", \"hummus\", \"clap\", \"scooter\", \"pool\", \"ravi\", \"laughter\", \"day\", \"year\",\n",
        "                     \"marshmallow\", \"gonzale\", \"roxy\", \"tiktok\", \"chicka\", \"romper\", \"booger\", \"dracula\",  \"cupid\",\n",
        "                      \"daddy\", \"btch\", \"ass\", \"mama\", \"folk\", \"snoop\", \"weed\", \"applause\",\n",
        "                     \"cause\", \"baby\", \"bit\", \"joke\", \"clapping\", \"whoo\",\n",
        "                      \"ox\", \"bolsonaro\", \"sheep\", \"liner\", \"frog\", \"tiger\", \"ruie\", \"brazil\",\"cunt\",\"show\",\"pussy\",\n",
        "                     \"fck\",\"fcking\",\"gon\",\"filmstar\",\"rowan\",\"headmaster\",\"perkin\",\"thbpth\",\"figaro\",\"bioody\",\n",
        "                     \"piague\",\"hoddededede\",\"raptor\",\"tit\",\"yknow\",\"bleep\",\"daggum\",\"gitrdone\",\"parasite\",\"bandage\"]"
      ],
      "metadata": {
        "id": "t2vrlWF5jJDf"
      },
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_lemmatized = [[word for word in doc if word not in custom_stop_words] for doc in data_lemmatized]"
      ],
      "metadata": {
        "id": "4C8elHd5MS-5"
      },
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "id2word = corpora.Dictionary(data_lemmatized)"
      ],
      "metadata": {
        "id": "MS2DSjWa0xJ-"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "texts = data_lemmatized"
      ],
      "metadata": {
        "id": "jBJi9Sc71mQS"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corpus = [id2word.doc2bow(text) for text in texts] #termdocumentfrequency"
      ],
      "metadata": {
        "id": "7V2NCCxJ1uD3"
      },
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]]"
      ],
      "metadata": {
        "id": "QvDq-HNA1vNB"
      },
      "execution_count": 294,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
        "                                           id2word=id2word,\n",
        "                                           num_topics=4,\n",
        "                                           random_state=100,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=10,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)"
      ],
      "metadata": {
        "id": "XqmcAg6A1xT2"
      },
      "execution_count": 330,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint"
      ],
      "metadata": {
        "id": "v6DmksZv5wQM"
      },
      "execution_count": 328,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(lda_model.print_topics())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQFMpaK82GbI",
        "outputId": "88611c95-31aa-4457-de17-bd5d4c796598"
      },
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0,\n",
            "  '0.002*\"woive\" + 0.002*\"evoiution\" + 0.002*\"hannibai\" + 0.001*\"thinker\" + '\n",
            "  '0.001*\"fiim\" + 0.001*\"battie\" + 0.001*\"eiephant\" + 0.001*\"ciever\" + '\n",
            "  '0.001*\"spartan\" + 0.001*\"farming\"'),\n",
            " (1,\n",
            "  '0.014*\"people\" + 0.012*\"mate\" + 0.008*\"mum\" + 0.006*\"man\" + 0.005*\"dog\" + '\n",
            "  '0.005*\"world\" + 0.005*\"film\" + 0.005*\"cat\" + 0.005*\"sort\" + 0.005*\"woman\"'),\n",
            " (2,\n",
            "  '0.040*\"people\" + 0.018*\"man\" + 0.009*\"guy\" + 0.008*\"country\" + '\n",
            "  '0.007*\"world\" + 0.005*\"money\" + 0.005*\"life\" + 0.004*\"car\" + 0.004*\"name\" + '\n",
            "  '0.004*\"kid\"'),\n",
            " (3,\n",
            "  '0.025*\"guy\" + 0.021*\"people\" + 0.020*\"man\" + 0.014*\"woman\" + 0.010*\"kid\" + '\n",
            "  '0.009*\"life\" + 0.009*\"girl\" + 0.008*\"friend\" + 0.007*\"love\" + 0.005*\"dog\"'),\n",
            " (4,\n",
            "  '0.001*\"stalker\" + 0.001*\"pubis\" + 0.001*\"shutdown\" + 0.001*\"cybersecurity\" '\n",
            "  '+ 0.001*\"thatch\" + 0.001*\"tasty\" + 0.001*\"leonardo\" + 0.001*\"rebounder\" + '\n",
            "  '0.000*\"bulb\" + 0.000*\"poopy\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_coherence_values_test(dictionary, corpus, texts, limit, start=2, step=1):\n",
        "    coherence_values = []\n",
        "    model_list = []\n",
        "    for num_topics in range(start, limit, step):\n",
        "        model = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, random_state=100,\n",
        "                                                chunksize=100, passes=10, per_word_topics=True, id2word=id2word)\n",
        "\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\n"
      ],
      "metadata": {
        "id": "u0x-YEXV2dGV"
      },
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Jahu_svP0JpA"
      },
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_list, coherence_values = compute_coherence_values_test(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=8, step=1)\n",
        "\n",
        "# Show graph\n",
        "limit = 8; start = 2; step = 1\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()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JUHFc3d7RDID",
        "outputId": "580404d7-b339-406d-9619-7db6332f438d"
      },
      "execution_count": 326,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Baseline model - n_topics = 4"
      ],
      "metadata": {
        "id": "ftSE2_WR3LsF"
      },
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function with all hyperparameters included"
      ],
      "metadata": {
        "id": "dZRG86pA3X1y"
      },
      "execution_count": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import gensim\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "def compute_coherence_values(dictionary, corpus, texts, alpha, beta, start, limit, step):\n",
        "    coherence_values = []\n",
        "    model_list = []\n",
        "    for num_topics in range(start, limit, step):\n",
        "        model = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary,\n",
        "                                                alpha=alpha, eta=beta, random_state=100,\n",
        "                                                chunksize=100, passes=10, per_word_topics=True)\n",
        "\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\n",
        "\n",
        "\n",
        "alpha_values = ['symmetric', 'asymmetric', 0.01, 0.1, 0.5]\n",
        "beta_values = ['symmetric', 0.01, 0.1, 0.3, 0.5]\n",
        "\n",
        "\n",
        "limit = 6; start = 2; step = 1\n",
        "x = range(start, limit, step)\n",
        "\n",
        "fig, axes = plt.subplots(nrows=5, ncols=1, figsize=(10, 20))\n",
        "\n",
        "for i, (alpha, beta) in enumerate(zip(alpha_values, beta_values)):\n",
        "    _, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized,\n",
        "                                                   alpha=alpha, beta=beta, start=start, limit=limit, step=step)\n",
        "\n",
        "    axes[i].plot(x, coherence_values, marker='o')\n",
        "    axes[i].set_title(f'Alpha: {alpha} / Beta: {beta}')\n",
        "    axes[i].set_xlabel(\"Num Topics\")\n",
        "    axes[i].set_ylabel(\"Coherence score\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "LHebw09pReA6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3edcd205-c874-4f6f-fa5d-1199b41851b3"
      },
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
        "                                           id2word=id2word,\n",
        "                                           num_topics=4,\n",
        "                                           random_state=100,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=20,\n",
        "                                           alpha=0.5,eta = 0.5,\n",
        "                                           per_word_topics=True)"
      ],
      "metadata": {
        "id": "bxXnd6Iy3m1u"
      },
      "execution_count": 400,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(lda_model.print_topics())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jp_pvMEDQwQ6",
        "outputId": "8e16bc76-62e5-49e6-f87d-c3d564629039"
      },
      "execution_count": 401,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0,\n",
            "  '0.001*\"evoiution\" + 0.001*\"woive\" + 0.001*\"farming\" + 0.001*\"thinker\" + '\n",
            "  '0.001*\"hannibai\" + 0.001*\"trumpet\" + 0.001*\"ciever\" + 0.001*\"eiephant\" + '\n",
            "  '0.001*\"fiim\" + 0.001*\"battie\"'),\n",
            " (1,\n",
            "  '0.015*\"mate\" + 0.010*\"mum\" + 0.005*\"film\" + 0.004*\"mime\" + 0.004*\"bloke\" + '\n",
            "  '0.004*\"sort\" + 0.003*\"panda\" + 0.003*\"lad\" + 0.003*\"arse\" + '\n",
            "  '0.002*\"glasgow\"'),\n",
            " (2,\n",
            "  '0.026*\"people\" + 0.012*\"country\" + 0.007*\"world\" + 0.005*\"war\" + '\n",
            "  '0.005*\"drug\" + 0.004*\"president\" + 0.004*\"news\" + 0.004*\"government\" + '\n",
            "  '0.004*\"gun\" + 0.003*\"police\"'),\n",
            " (3,\n",
            "  '0.026*\"people\" + 0.021*\"guy\" + 0.021*\"man\" + 0.012*\"woman\" + 0.009*\"kid\" + '\n",
            "  '0.009*\"life\" + 0.007*\"girl\" + 0.007*\"friend\" + 0.006*\"love\" + '\n",
            "  '0.005*\"night\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "R3lpYV094SbF"
      },
      "execution_count": 402,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W-uIRqJcSsJN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}