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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/claudio1975/SWISSTEXT_2022/blob/main/NLP_text_analysis_v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **NLP_&_INSURANCE: TEXT_ANALYSIS**"
      ],
      "metadata": {
        "id": "liWb_eKRH-sQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://miro.medium.com/max/3310/1*2uLP1885m7LG9f638JlLGQ.png)"
      ],
      "metadata": {
        "id": "iDPfXei3_LAe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries"
      ],
      "metadata": {
        "id": "mP5_Yn4wCaru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bertopic"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20PmpNKoZjGE",
        "outputId": "75edaa12-e0d5-4f3c-ac3f-fc146c7195e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bertopic\n",
            "  Downloading bertopic-0.13.0-py2.py3-none-any.whl (103 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.9/103.9 KB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: plotly>=4.7.0 in /usr/local/lib/python3.8/dist-packages (from bertopic) (5.5.0)\n",
            "Requirement already satisfied: scikit-learn>=0.22.2.post1 in /usr/local/lib/python3.8/dist-packages (from bertopic) (1.0.2)\n",
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            "Collecting hdbscan>=0.8.29\n",
            "  Downloading hdbscan-0.8.29.tar.gz (5.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m69.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.8/dist-packages (from bertopic) (1.21.6)\n",
            "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.8/dist-packages (from bertopic) (4.64.1)\n",
            "Collecting sentence-transformers>=0.4.1\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 KB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pyyaml<6.0\n",
            "  Downloading PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m662.4/662.4 KB\u001b[0m \u001b[31m47.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting umap-learn>=0.5.0\n",
            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.2/88.2 KB\u001b[0m \u001b[31m11.7 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: cython>=0.27 in /usr/local/lib/python3.8/dist-packages (from hdbscan>=0.8.29->bertopic) (0.29.33)\n",
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            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.1.5->bertopic) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas>=1.1.5->bertopic) (2022.7)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from plotly>=4.7.0->bertopic) (8.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from plotly>=4.7.0->bertopic) (1.15.0)\n",
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            "Collecting transformers<5.0.0,>=4.6.0\n",
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            "  Downloading pynndescent-0.5.8.tar.gz (1.1 MB)\n",
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            "Building wheels for collected packages: hdbscan, sentence-transformers, umap-learn, pynndescent\n",
            "  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hdbscan: filename=hdbscan-0.8.29-cp38-cp38-linux_x86_64.whl size=3773942 sha256=0a2e5a13c44bb08ff73424122a1fe74de6cdd7955d9423ebf06a099888964dec\n",
            "  Stored in directory: /root/.cache/pip/wheels/76/06/48/527e038689c581cc9e519c73840efdc7473805149e55bd7ffd\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=80377e0b17f6954b969612d0c669418b85e5b37df42910a06aff25a2681df82f\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/6f/8c/d88aec621f3f542d26fac0342bef5e693335d125f4e54aeffe\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82829 sha256=02aaaea985f32e66ddf2f7c7eee1bae359372b23cc19b9419d45b8475d8b1544\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/3a/67/06a8950e053725912e6a8c42c4a3a241410f6487b8402542ea\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.8-py3-none-any.whl size=55513 sha256=3447ae369153a12992d180add6463ed8145fe293db60e7678344e13f5fdea7a3\n",
            "  Stored in directory: /root/.cache/pip/wheels/1c/63/3a/29954bca1a27ba100ed8c27973a78cb71b43dc67aed62e80c3\n",
            "Successfully built hdbscan sentence-transformers umap-learn pynndescent\n",
            "Installing collected packages: tokenizers, sentencepiece, pyyaml, huggingface-hub, transformers, pynndescent, hdbscan, umap-learn, sentence-transformers, bertopic\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "Successfully installed bertopic-0.13.0 hdbscan-0.8.29 huggingface-hub-0.12.0 pynndescent-0.5.8 pyyaml-5.4.1 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.0 umap-learn-0.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bertopic[visualization]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fIsayhvZjMl",
        "outputId": "e5ee6c1a-b279-479c-e97b-4b35cd6404ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bertopic[visualization] in /usr/local/lib/python3.8/dist-packages (0.13.0)\n",
            "\u001b[33mWARNING: bertopic 0.13.0 does not provide the extra 'visualization'\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: scikit-learn>=0.22.2.post1 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (1.0.2)\n",
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            "Requirement already satisfied: hdbscan>=0.8.29 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (0.8.29)\n",
            "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (1.3.5)\n",
            "Requirement already satisfied: pyyaml<6.0 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (5.4.1)\n",
            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (1.21.6)\n",
            "Requirement already satisfied: sentence-transformers>=0.4.1 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (2.2.2)\n",
            "Requirement already satisfied: plotly>=4.7.0 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (5.5.0)\n",
            "Requirement already satisfied: tqdm>=4.41.1 in /usr/local/lib/python3.8/dist-packages (from bertopic[visualization]) (4.64.1)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.22.2.post1->bertopic[visualization]) (3.1.0)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.8/dist-packages (from sentence-transformers>=0.4.1->bertopic[visualization]) (0.1.97)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (from sentence-transformers>=0.4.1->bertopic[visualization]) (0.14.1+cu116)\n",
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            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.8/dist-packages (from sentence-transformers>=0.4.1->bertopic[visualization]) (4.26.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.8/dist-packages (from sentence-transformers>=0.4.1->bertopic[visualization]) (1.13.1+cu116)\n",
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            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.8/dist-packages (from umap-learn>=0.5.0->bertopic[visualization]) (0.5.8)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.8/dist-packages (from umap-learn>=0.5.0->bertopic[visualization]) (0.56.4)\n",
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            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (2.25.1)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn>=0.5.0->bertopic[visualization]) (6.0.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn>=0.5.0->bertopic[visualization]) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn>=0.5.0->bertopic[visualization]) (57.4.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic[visualization]) (2022.6.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.4.1->bertopic[visualization]) (0.13.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from nltk->sentence-transformers>=0.4.1->bertopic[visualization]) (7.1.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision->sentence-transformers>=0.4.1->bertopic[visualization]) (7.1.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba>=0.49->umap-learn>=0.5.0->bertopic[visualization]) (3.11.0)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (4.0.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.4.1->bertopic[visualization]) (2022.12.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#setup deterministic results \n",
        "import numpy as np\n",
        "from numpy.random import seed\n",
        "seed=0\n",
        "np.random.seed(seed)\n",
        "import tensorflow as tf\n",
        "tf.random.set_seed(seed)\n",
        "import os\n",
        "os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
        "\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.decomposition import PCA\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import wordcloud\n",
        "from wordcloud import WordCloud, STOPWORDS\n",
        "import re\n",
        "import string\n",
        "#import langdetect \n",
        "#import ktrain\n",
        "#from ktrain import text\n",
        "import spacy\n",
        "import gensim\n",
        "import gensim.corpora as corpora\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "from gensim.models import Word2Vec \n",
        "import gensim.downloader as gensim_api\n",
        "from bertopic import BERTopic\n",
        "from pprint import pprint"
      ],
      "metadata": {
        "id": "fs11-otnJH6u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bb92219-2efd-4a42-ed3a-774cd73c3425"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:497: UserWarning: Can't initialize NVML\n",
            "  warnings.warn(\"Can't initialize NVML\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Requirements"
      ],
      "metadata": {
        "id": "cksn-dSFNzmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.__version__) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGmRQbDsJqI-",
        "outputId": "6e499065-6724-4757-8bc1-59c39df17cc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.21.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.__version__) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ge7646ZbLSkn",
        "outputId": "cbdd3d9d-e005-4fb6-b24d-91bdf73245f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sklearn.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wY4eVqHRLSuK",
        "outputId": "dde909c2-49b3-4dab-9f1d-18dbf9c54ee9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sns.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQFoDoZJLSw-",
        "outputId": "37565874-10b2-4c67-8cac-c8feedf0be45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.11.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHkBAE00LS4-",
        "outputId": "aa63fa94-2073-4791-c2a0-8cde4398e4cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(spacy.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nt7-AB_JBeuG",
        "outputId": "3ae1523f-aebf-47d5-e24f-c7cbcf276b41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.4.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(gensim.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siNtLDbkuVGG",
        "outputId": "f10bc539-ebcc-4fd0-b78d-49955c0d9166"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.6.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upload dataset"
      ],
      "metadata": {
        "id": "7cl-fs32LpJ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.kaggle.com/competitions/nlp-getting-started"
      ],
      "metadata": {
        "id": "lBU-GaZdHZmn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_2 = pd.read_csv('https://raw.githubusercontent.com/claudio1975/SWISSTEXT_2022/main/data/train_clean.csv')"
      ],
      "metadata": {
        "id": "TmqNgXQiBiDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from google.colab import files \n",
        "#uploaded = files.upload() "
      ],
      "metadata": {
        "id": "t9QumHJqLS7q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reading File\n",
        "#df_2 = pd.read_csv('train_clean.csv')"
      ],
      "metadata": {
        "id": "zm6ikNARLS-C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# select columns\n",
        "df_2 = df_2[['id','keyword','location','text','target','lang']]\n",
        "df_2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rqdmz3Jwvr3s",
        "outputId": "42ea198a-a375-467f-a74b-c5d0b1a07a1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              "\n",
              "   target lang  \n",
              "0       1   en  \n",
              "1       1   en  \n",
              "2       1   en  \n",
              "3       1   en  \n",
              "4       1   en  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2f1eea2-3c68-4ca3-bed2-693e2181b39b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>location</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
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              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
              "      <td>1</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b2f1eea2-3c68-4ca3-bed2-693e2181b39b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b2f1eea2-3c68-4ca3-bed2-693e2181b39b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Named Entity Recognition (NER) and Part of Speech Tagging (POS)"
      ],
      "metadata": {
        "id": "v95PdPJAX1I8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load(\"en_core_web_sm\")"
      ],
      "metadata": {
        "id": "K5Fxq2kri4Fm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ner_lst = nlp.pipe_labels['ner']\n",
        "print(len(ner_lst))\n",
        "print(ner_lst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BAxVZUAiekng",
        "outputId": "aebc9b57-c884-497e-ea25-2ee74421f163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18\n",
            "['CARDINAL', 'DATE', 'EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'MONEY', 'NORP', 'ORDINAL', 'ORG', 'PERCENT', 'PERSON', 'PRODUCT', 'QUANTITY', 'TIME', 'WORK_OF_ART']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tag text cleaned\n",
        "txt = df_2[\"text\"].iloc[5]\n",
        "doc_cleaned = nlp(txt)\n",
        "# display result\n",
        "#spacy.displacy.render(doc_cleaned, style=\"ent\")\n",
        "print(txt)\n",
        "print([(X.text, X.label_) for X in doc_cleaned.ents])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1cp4NXsX4-u",
        "outputId": "4e46e5a2-6007-41c7-8c68-abee2d331b55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#RockyFire Update => California Hwy. 20 closed in both directions due to Lake County fire - #CAfire #wildfires\n",
            "[('#RockyFire Update', 'MONEY'), ('California', 'GPE'), ('20', 'CARDINAL'), ('Lake County', 'GPE'), ('#CAfire #', 'MONEY')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{'text':{15}} {'NER':{10}} {'NER explained':{20}} \")\n",
        "for ent in doc_cleaned.ents:\n",
        "  print(f'{ent.text:{15}} {ent.label_:{10}} {spacy.explain(ent.label_):{20}}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skq4COdadns9",
        "outputId": "1e428174-dbed-4632-d215-ea48f7e74175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text            NER        NER explained        \n",
            "#RockyFire Update MONEY      Monetary values, including unit\n",
            "California      GPE        Countries, cities, states\n",
            "20              CARDINAL   Numerals that do not fall under another type\n",
            "Lake County     GPE        Countries, cities, states\n",
            "#CAfire #       MONEY      Monetary values, including unit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tag_lst = nlp.pipe_labels['tagger']\n",
        "print(len(tag_lst))\n",
        "print(tag_lst)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7c_WSK4gnpP",
        "outputId": "55752ee9-7e3f-4d79-85da-b1fda6a4953d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50\n",
            "['$', \"''\", ',', '-LRB-', '-RRB-', '.', ':', 'ADD', 'AFX', 'CC', 'CD', 'DT', 'EX', 'FW', 'HYPH', 'IN', 'JJ', 'JJR', 'JJS', 'LS', 'MD', 'NFP', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'POS', 'PRP', 'PRP$', 'RB', 'RBR', 'RBS', 'RP', 'SYM', 'TO', 'UH', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WP$', 'WRB', 'XX', '_SP', '``']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(txt)\n",
        "print([(X.text, X.pos_, X.tag_) for X in doc_cleaned])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVoACyVCiVwz",
        "outputId": "e8c03bec-8f39-440d-8d62-d156c631ad6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#RockyFire Update => California Hwy. 20 closed in both directions due to Lake County fire - #CAfire #wildfires\n",
            "[('#', 'SYM', '$'), ('RockyFire', 'VERB', 'VB'), ('Update', 'PROPN', 'NNP'), ('=', 'NOUN', 'NN'), ('>', 'X', 'XX'), ('California', 'PROPN', 'NNP'), ('Hwy', 'PROPN', 'NNP'), ('.', 'PROPN', 'NNP'), ('20', 'NUM', 'CD'), ('closed', 'VERB', 'VBD'), ('in', 'ADP', 'IN'), ('both', 'DET', 'DT'), ('directions', 'NOUN', 'NNS'), ('due', 'ADP', 'IN'), ('to', 'ADP', 'IN'), ('Lake', 'PROPN', 'NNP'), ('County', 'PROPN', 'NNP'), ('fire', 'NOUN', 'NN'), ('-', 'PUNCT', ':'), ('#', 'NOUN', 'NN'), ('CAfire', 'NOUN', 'NN'), ('#', 'NOUN', 'NN'), ('wildfires', 'VERB', 'VBZ')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"{'text':{10}} {'POS':{10}} {'TAG':{10}} {'POS explained':{20}} {'tag explained'} \")\n",
        "for token in doc_cleaned:\n",
        "  print(f'{token.text:{10}} {token.pos_:{10}} {token.tag_:{10}} {spacy.explain(token.pos_):{20}} {spacy.explain(token.tag_)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6gOnftZakbH",
        "outputId": "ebd3c21f-40e1-41bb-b7b1-f6ba271f34f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "text       POS        TAG        POS explained        tag explained \n",
            "#          SYM        $          symbol               symbol, currency\n",
            "RockyFire  VERB       VB         verb                 verb, base form\n",
            "Update     PROPN      NNP        proper noun          noun, proper singular\n",
            "=          NOUN       NN         noun                 noun, singular or mass\n",
            ">          X          XX         other                unknown\n",
            "California PROPN      NNP        proper noun          noun, proper singular\n",
            "Hwy        PROPN      NNP        proper noun          noun, proper singular\n",
            ".          PROPN      NNP        proper noun          noun, proper singular\n",
            "20         NUM        CD         numeral              cardinal number\n",
            "closed     VERB       VBD        verb                 verb, past tense\n",
            "in         ADP        IN         adposition           conjunction, subordinating or preposition\n",
            "both       DET        DT         determiner           determiner\n",
            "directions NOUN       NNS        noun                 noun, plural\n",
            "due        ADP        IN         adposition           conjunction, subordinating or preposition\n",
            "to         ADP        IN         adposition           conjunction, subordinating or preposition\n",
            "Lake       PROPN      NNP        proper noun          noun, proper singular\n",
            "County     PROPN      NNP        proper noun          noun, proper singular\n",
            "fire       NOUN       NN         noun                 noun, singular or mass\n",
            "-          PUNCT      :          punctuation          punctuation mark, colon or ellipsis\n",
            "#          NOUN       NN         noun                 noun, singular or mass\n",
            "CAfire     NOUN       NN         noun                 noun, singular or mass\n",
            "#          NOUN       NN         noun                 noun, singular or mass\n",
            "wildfires  VERB       VBZ        verb                 verb, 3rd person singular present\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text pre-processing"
      ],
      "metadata": {
        "id": "qxCD3Np7Mq8x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lemmatizer(text):        \n",
        "    lem = []\n",
        "    doc = nlp(text)\n",
        "    for word in doc:\n",
        "        lem.append(word.lemma_)\n",
        "    return \" \".join(lem)\n",
        "def clean_text(text):\n",
        "    # Normalize Text\n",
        "    text = text.lower()\n",
        "    # Remove Unicode Characters\n",
        "    text = re.sub(r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?\", '',text)\n",
        "    # Remove Punctuaction\n",
        "    text = re.sub(r'[%s]' % re.escape(string.punctuation), '', text)\n",
        "    # Remove words containing numbers\n",
        "    text = re.sub(r'\\w*\\d\\w*', '', text) \n",
        "    # Remove Stopwords\n",
        "    all_stop_words = nlp.Defaults.stop_words\n",
        "    text = \" \".join([word for word in str(text).split() if word not in all_stop_words])\n",
        "    # Lemmatization\n",
        "    text = lemmatizer(text)\n",
        "    text = re.sub(r'-PRON-', '',text)\n",
        "    return text\n"
      ],
      "metadata": {
        "id": "e42fL9RDNKbt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i=3\n",
        "df_cleaned = pd.DataFrame(df_2.apply(lambda x: clean_text(x.iloc[i]), axis=1), columns = ['text_cleaned'])\n"
      ],
      "metadata": {
        "id": "T6Efomx4XYHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_3 = pd.concat([df_2, df_cleaned], axis=1)\n",
        "df_3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "srZyAxKGMj2l",
        "outputId": "f1b5b804-eb0c-4574-994f-140e97df404f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
              "\n",
              "   target lang                                       text_cleaned  \n",
              "0       1   en               deed reason earthquake allah forgive  \n",
              "1       1   en              forest fire near la ronge sask canada  \n",
              "2       1   en  resident ask shelter place notify officer evac...  \n",
              "3       1   en  people receive wildfire evacuation order calif...  \n",
              "4       1   en  got send photo ruby alaska smoke wildfire pour...  "
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>location</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>lang</th>\n",
              "      <th>text_cleaned</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>deed reason earthquake allah forgive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>forest fire near la ronge sask canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>All residents asked to 'shelter in place' are ...</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>resident ask shelter place notify officer evac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>people receive wildfire evacuation order calif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>got send photo ruby alaska smoke wildfire pour...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5cdcca4c-74e8-461e-b967-2bca169d146b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5cdcca4c-74e8-461e-b967-2bca169d146b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5cdcca4c-74e8-461e-b967-2bca169d146b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_3['text'][5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "3hBCDBT4ywjb",
        "outputId": "23461274-21b9-4475-af62-4aa9be642a54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'#RockyFire Update => California Hwy. 20 closed in both directions due to Lake County fire - #CAfire #wildfires'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_3['text_cleaned'][5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "wmGW_oZHzB1a",
        "outputId": "fdd2f515-4c43-4bc4-c4da-9337b7c0f418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'rockyfire update california hwy closed directions lake county fire cafire wildfire'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## N-Grams"
      ],
      "metadata": {
        "id": "I1oyUHh3A6FB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top_n_words(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(1,1),                      # n_gram_type\n",
        "                          token_pattern= r'([a-zA-Z0-9-/]{5,})',  # num chars >= 5\n",
        "                          min_df=2).fit(corpus)                   # minimum required occurences of a word      \n",
        "    bag_of_words = vec.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
        "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
        "    return words_freq[:n]\n",
        "common_words = get_top_n_words(df_3['text_cleaned'], 20)\n",
        "df_uni = pd.DataFrame(common_words, columns = ['unigram' , 'count'])"
      ],
      "metadata": {
        "id": "oUr24X4fYCN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=df_uni['unigram'], y=df_uni['count'])\n",
        "plt.ylabel('Number of Occurrences', fontsize=10)\n",
        "plt.xlabel('Unigram', fontsize=10)\n",
        "plt.xticks(rotation=60)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "UYc2YoNcBOUt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "e4ab5e34-8ca9-468f-cdb8-90e6013d50f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top_n_words(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(2,2),                      # n_gram_type\n",
        "                          token_pattern= r'([a-zA-Z0-9-/]{5,})',  # num chars >= 5\n",
        "                          min_df=2).fit(corpus)                   # minimum required occurences of a word      \n",
        "    bag_of_words = vec.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
        "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
        "    return words_freq[:n]\n",
        "common_words = get_top_n_words(df_3['text_cleaned'], 20)\n",
        "df_bi = pd.DataFrame(common_words, columns = ['bigram' , 'count'])"
      ],
      "metadata": {
        "id": "7PJL3iHxBOXa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=df_bi['bigram'], y=df_bi['count'])\n",
        "plt.ylabel('Number of Occurrences', fontsize=10)\n",
        "plt.xlabel('Bigram', fontsize=10)\n",
        "plt.xticks(rotation=60)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "04kqVlrwJpCs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "5eb9bb80-8500-49e1-b8c1-0c6e62985796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top_n_words(corpus, n=None):\n",
        "    vec = CountVectorizer(ngram_range=(3,3),                     # n_gram_type\n",
        "                          token_pattern= r'([a-zA-Z0-9-/]{5,})', # num chars >= 5\n",
        "                          min_df=2).fit(corpus)                  # minimum required occurences of a word      \n",
        "    bag_of_words = vec.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
        "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
        "    return words_freq[:n]\n",
        "common_words = get_top_n_words(df_3['text_cleaned'], 20)\n",
        "df_tri = pd.DataFrame(common_words, columns = ['trigram' , 'count'])"
      ],
      "metadata": {
        "id": "_qc9M6ydEPaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=df_tri['trigram'], y=df_tri['count'])\n",
        "plt.ylabel('Number of Occurrences', fontsize=10)\n",
        "plt.xlabel('Trigram', fontsize=10)\n",
        "plt.xticks(rotation=60)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "yC2ryYdFEbW8",
        "outputId": "ac13826c-cc8a-48d6-c6ab-6dded298a5e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Word Embedding"
      ],
      "metadata": {
        "id": "ywIwC1luRVW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize(sentence):\n",
        "  sentence = str(sentence).replace(\"\\n\", \" \")\n",
        "  token = nlp(sentence)\n",
        "  tokens = [w.text for w in token]\n",
        "  # remove short tokens\n",
        "  tokens = [t for t in tokens if len(t) > 4]\n",
        "  return tokens"
      ],
      "metadata": {
        "id": "RFSLtU_NT718"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = df_3['text_cleaned'].apply(tokenize)\n",
        "def vocabulary(doc):        \n",
        "    corpus = []\n",
        "    for word in doc:\n",
        "        corpus.append(word)\n",
        "    return corpus"
      ],
      "metadata": {
        "id": "mtX7b8UrRf3c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corpus = vocabulary(data)"
      ],
      "metadata": {
        "id": "n6jKwA7ERoFO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def similar(model, word):\n",
        "    return model.wv.most_similar(word) \n",
        "\n",
        "def embedding(model, word):\n",
        "    return model.wv[word]\n",
        "\n",
        "def similarity(model, word1, word2):\n",
        "    return model.wv.similarity(word1, word2)"
      ],
      "metadata": {
        "id": "OVESLuQbRqVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CBOW Model"
      ],
      "metadata": {
        "id": "7zGvuJX2t7oY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://media.geeksforgeeks.org/wp-content/uploads/cbow-1.png)"
      ],
      "metadata": {
        "id": "9mBx6MUtt-Lf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create CBOW model\n",
        "model_cbow = Word2Vec(corpus, min_count = 50)"
      ],
      "metadata": {
        "id": "cCJPV9t3Rs5R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcd69f08-99da-4dd6-d491-3fb30b3ddbad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.base_any2vec:under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_cbow)"
      ],
      "metadata": {
        "id": "VM3LyCAYRvkm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "742b2b47-9b09-4d9e-9226-ddda45a03be2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word2Vec(vocab=99, size=100, alpha=0.025)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize vocabulary\n",
        "words = list(model_cbow.wv.vocab)\n",
        "print(words)"
      ],
      "metadata": {
        "id": "1Rx3oVwYjig6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bc80158-a232-454a-ff42-8e7004cf6bf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['earthquake', 'forest', 'evacuation', 'people', 'wildfire', 'california', 'photo', 'school', 'update', 'flood', 'disaster', 'cause', 'emergency', 'happen', 'damage', 'crash', 'break', 'night', 'police', 'thank', 'leave', 'black', 'train', 'truck', 'video', 'accident', 'great', 'report', 'cross', 'injury', 'right', 'change', 'today', 'issue', 'scream', 'youtube', 'thing', 'debris', 'family', 'state', 'wreck', 'death', 'house', 'service', 'destroy', 'think', 'siren', 'weather', 'world', 'attack', 'survive', 'watch', 'start', 'movie', 'storm', 'collapse', 'military', 'japan', 'charge', 'northern', 'nuclear', 'suicide', 'injure', 'obama', 'weapon', 'release', 'woman', 'atomic', 'flame', 'hostage', 'building', 'wound', 'drown', 'panic', 'hiroshima', 'bombing', 'rescue', 'explode', 'evacuate', 'confirm', 'content', 'water', 'casualty', 'collide', 'thunderstorm', 'fatal', 'crush', 'legionnaire', 'malaysia', 'deluge', 'demolish', 'derail', 'detonate', 'electrocute', 'reddit', 'fatality', 'obliterate', 'quarantine', 'bomber']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'earthquake')"
      ],
      "metadata": {
        "id": "wrFNdj6PRwDi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d3a13da-8228-4ad4-df01-5cfde0ae0881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('house', 0.3591579794883728),\n",
              " ('panic', 0.2768034338951111),\n",
              " ('death', 0.2711658477783203),\n",
              " ('nuclear', 0.2659183442592621),\n",
              " ('think', 0.25583887100219727),\n",
              " ('damage', 0.23612067103385925),\n",
              " ('electrocute', 0.21050655841827393),\n",
              " ('japan', 0.1991567760705948),\n",
              " ('california', 0.199130117893219),\n",
              " ('watch', 0.1940617561340332)]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding(model_cbow, 'earthquake')"
      ],
      "metadata": {
        "id": "CvCZ-zkkRyDh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94266701-ef8f-4ec8-a82b-79ac7db240f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.7843701e-04, -2.2559525e-03,  5.3317610e-05,  4.2542364e-03,\n",
              "        4.2312220e-03,  3.1191686e-03,  3.2023883e-03,  1.9392136e-03,\n",
              "        3.8418528e-03,  4.6705413e-03, -4.2363401e-03, -3.1760810e-03,\n",
              "        3.3921709e-03,  2.4335524e-03,  2.4735832e-03,  1.9559080e-03,\n",
              "       -7.1679812e-04, -2.8555229e-04, -5.4391706e-03, -1.0449545e-03,\n",
              "        6.2117429e-04,  4.5187511e-03, -2.2326468e-03, -4.1124122e-03,\n",
              "       -5.0435705e-05,  1.5564887e-03,  9.8562297e-05,  2.7308920e-03,\n",
              "       -2.7206906e-03,  3.0956748e-03, -4.4824304e-03,  5.0379639e-04,\n",
              "       -3.7812989e-03,  3.7493946e-03, -3.2425248e-03, -3.4645584e-03,\n",
              "       -4.9449210e-03,  3.4431284e-03,  4.3921587e-03, -2.7764805e-03,\n",
              "        3.4683782e-03,  2.4529537e-03,  1.4998852e-03,  2.3244908e-04,\n",
              "       -1.1290312e-03, -2.9344663e-03, -1.2243621e-03,  4.3519735e-03,\n",
              "       -9.7803667e-04, -5.1525901e-03,  1.0894542e-03, -1.2891905e-03,\n",
              "       -3.8833236e-03, -4.9908215e-04,  5.6062788e-03,  2.0869365e-03,\n",
              "       -1.7730660e-03, -3.6028838e-03,  2.1350151e-03, -2.8786955e-03,\n",
              "        1.9037995e-03,  3.1879803e-03,  4.7249999e-03, -1.6025378e-04,\n",
              "       -3.0607670e-03, -1.8736587e-03,  2.8361383e-04,  1.1566372e-03,\n",
              "        4.2689377e-03,  3.7136090e-03, -5.6658284e-04, -8.9496881e-04,\n",
              "       -1.8154782e-04,  3.0425331e-04, -4.5766318e-04, -7.7155337e-04,\n",
              "       -7.4781489e-04,  1.4912196e-03, -5.8459514e-04,  3.0338956e-04,\n",
              "       -3.3459868e-03,  2.8794997e-03,  1.7172471e-03, -2.1884954e-03,\n",
              "       -6.7402475e-04, -1.5749746e-03, -1.3294466e-03,  3.6325303e-03,\n",
              "       -2.8148307e-03,  7.1822543e-04, -4.1874107e-03, -1.3968650e-03,\n",
              "       -4.3566404e-03, -8.1061816e-04,  4.8514167e-03,  1.0982035e-03,\n",
              "        4.3038841e-04,  2.8358039e-03,  3.9566108e-03,  2.7227565e-03],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFvbLfUCERs5",
        "outputId": "deea1276-6f44-49e8-8749-05f990803bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('water', 0.296151727437973),\n",
              " ('family', 0.2631525993347168),\n",
              " ('video', 0.24941644072532654),\n",
              " ('watch', 0.21095634996891022),\n",
              " ('truck', 0.20387795567512512),\n",
              " ('release', 0.19581447541713715),\n",
              " ('injure', 0.19446900486946106),\n",
              " ('emergency', 0.1889064610004425),\n",
              " ('hiroshima', 0.18269559741020203),\n",
              " ('reddit', 0.17561230063438416)]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'evacuation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4I_SHNKEeuq",
        "outputId": "ba5ac9b4-bf48-4466-95bd-06ec07cab49f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('obama', 0.277519553899765),\n",
              " ('hiroshima', 0.2551480233669281),\n",
              " ('video', 0.24541646242141724),\n",
              " ('release', 0.24467840790748596),\n",
              " ('start', 0.24075978994369507),\n",
              " ('atomic', 0.23403197526931763),\n",
              " ('legionnaire', 0.2210213840007782),\n",
              " ('rescue', 0.19891738891601562),\n",
              " ('wreck', 0.19148096442222595),\n",
              " ('flood', 0.18527206778526306)]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'wildfire')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Flbj1AdJE1Dg",
        "outputId": "0b7fdf54-9662-4eba-91da-f84cca6d53ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('bombing', 0.37278488278388977),\n",
              " ('confirm', 0.3553791642189026),\n",
              " ('crush', 0.2997792065143585),\n",
              " ('hiroshima', 0.28529825806617737),\n",
              " ('destroy', 0.285136342048645),\n",
              " ('flood', 0.2837890088558197),\n",
              " ('california', 0.27056998014450073),\n",
              " ('emergency', 0.2691180109977722),\n",
              " ('bomber', 0.26876360177993774),\n",
              " ('release', 0.2549082338809967)]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'disaster')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8USW5F7E681",
        "outputId": "7a463905-df7e-497d-e6c2-69af22955547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('video', 0.4579974114894867),\n",
              " ('world', 0.42336905002593994),\n",
              " ('obama', 0.3782280683517456),\n",
              " ('release', 0.3249501585960388),\n",
              " ('legionnaire', 0.2940165102481842),\n",
              " ('break', 0.28742778301239014),\n",
              " ('family', 0.28108495473861694),\n",
              " ('confirm', 0.2748374938964844),\n",
              " ('charge', 0.26838722825050354),\n",
              " ('reddit', 0.258894681930542)]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'emergency')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6yviiz4E8Zr",
        "outputId": "c8d5d5b4-553a-4ac4-913f-7cf6bf546715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('update', 0.3678016662597656),\n",
              " ('wildfire', 0.2691180408000946),\n",
              " ('truck', 0.25693345069885254),\n",
              " ('world', 0.25401049852371216),\n",
              " ('movie', 0.24439212679862976),\n",
              " ('collapse', 0.2424781769514084),\n",
              " ('great', 0.2365621030330658),\n",
              " ('bombing', 0.23386633396148682),\n",
              " ('family', 0.22497119009494781),\n",
              " ('black', 0.22396275401115417)]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_cbow, 'flood')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2xoQ7GMFJUm",
        "outputId": "3efead3c-e833-48cb-d9fc-e9183425e13d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('survive', 0.3204456865787506),\n",
              " ('bomber', 0.2950574457645416),\n",
              " ('people', 0.29479050636291504),\n",
              " ('wreck', 0.2847411632537842),\n",
              " ('crush', 0.28446245193481445),\n",
              " ('wildfire', 0.2837889790534973),\n",
              " ('bombing', 0.27110564708709717),\n",
              " ('quarantine', 0.2599281370639801),\n",
              " ('family', 0.2541256844997406),\n",
              " ('derail', 0.25259390473365784)]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_cbow, 'suicide', \"bomber\")"
      ],
      "metadata": {
        "id": "9M5DFWYRR0UK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c464e599-4858-4200-f925-46171e2214bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19414881"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_cbow, 'california', \"wildfire\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOF00tMZOvLU",
        "outputId": "8778c1c0-da0b-480a-85c0-15ebb32029e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.27056998"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_cbow, 'flood', \"disaster\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3cYnbZyOvPD",
        "outputId": "8402318b-e61f-4038-ae74-4c57f437f6bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.18810844"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = model_cbow.wv.vectors"
      ],
      "metadata": {
        "id": "WOH5knkUFhz1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=2)\n",
        "result = pca.fit_transform(X)"
      ],
      "metadata": {
        "id": "SK2hTFrokFRp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "plt.scatter(result[:, 0], result[:, 1])\n",
        "for i, word in enumerate(words):\n",
        "  plt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "j8TmKZzukZJb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "a4c6bdd1-6bfd-404c-a857-7c8fa47698e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Skip Gram Model"
      ],
      "metadata": {
        "id": "Yd_SFDpbuB44"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://media.geeksforgeeks.org/wp-content/uploads/skip_gram.png)"
      ],
      "metadata": {
        "id": "b_Dd_YN1uHsU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Skip Gram model\n",
        "model_sg = Word2Vec(corpus, min_count = 50, sg = 1)"
      ],
      "metadata": {
        "id": "mCx4i6RPRze-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "538f9c39-019d-4418-f5ab-f5d9c49f0a62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.base_any2vec:under 10 jobs per worker: consider setting a smaller `batch_words' for smoother alpha decay\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_sg)"
      ],
      "metadata": {
        "id": "HUyLhdD-R82V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7085fab4-6b84-45bd-8db2-c9f322a603fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Word2Vec(vocab=99, size=100, alpha=0.025)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize vocabulary\n",
        "words = list(model_sg.wv.vocab)\n",
        "print(words)"
      ],
      "metadata": {
        "id": "RVm5UwlEm20g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6f7d3d2-ba96-47e0-fa64-85440f85211f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['earthquake', 'forest', 'evacuation', 'people', 'wildfire', 'california', 'photo', 'school', 'update', 'flood', 'disaster', 'cause', 'emergency', 'happen', 'damage', 'crash', 'break', 'night', 'police', 'thank', 'leave', 'black', 'train', 'truck', 'video', 'accident', 'great', 'report', 'cross', 'injury', 'right', 'change', 'today', 'issue', 'scream', 'youtube', 'thing', 'debris', 'family', 'state', 'wreck', 'death', 'house', 'service', 'destroy', 'think', 'siren', 'weather', 'world', 'attack', 'survive', 'watch', 'start', 'movie', 'storm', 'collapse', 'military', 'japan', 'charge', 'northern', 'nuclear', 'suicide', 'injure', 'obama', 'weapon', 'release', 'woman', 'atomic', 'flame', 'hostage', 'building', 'wound', 'drown', 'panic', 'hiroshima', 'bombing', 'rescue', 'explode', 'evacuate', 'confirm', 'content', 'water', 'casualty', 'collide', 'thunderstorm', 'fatal', 'crush', 'legionnaire', 'malaysia', 'deluge', 'demolish', 'derail', 'detonate', 'electrocute', 'reddit', 'fatality', 'obliterate', 'quarantine', 'bomber']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'earthquake')"
      ],
      "metadata": {
        "id": "6ryBAeXlSAqK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cfbf582a-cb73-4c63-db59-5a1ba3348c9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('house', 0.40713250637054443),\n",
              " ('panic', 0.3077165484428406),\n",
              " ('nuclear', 0.29869383573532104),\n",
              " ('death', 0.2960660457611084),\n",
              " ('think', 0.2847999334335327),\n",
              " ('california', 0.28187745809555054),\n",
              " ('japan', 0.26151421666145325),\n",
              " ('damage', 0.25667309761047363),\n",
              " ('watch', 0.2399403303861618),\n",
              " ('wreck', 0.23055985569953918)]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding(model_sg, 'earthquake')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGaXhWecGvkr",
        "outputId": "7d3fc3ff-1dd4-43e5-cfc1-8bc8020715d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.2669152e-04, -2.2821899e-03, -4.3598333e-04,  4.5435713e-03,\n",
              "        4.4334880e-03,  3.0548323e-03,  3.3288116e-03,  1.4197984e-03,\n",
              "        3.8752074e-03,  4.8490660e-03, -3.7702722e-03, -3.2034996e-03,\n",
              "        2.8503905e-03,  2.3294701e-03,  2.4267267e-03,  1.8857324e-03,\n",
              "       -1.0115496e-03,  4.9191662e-05, -6.0563395e-03, -4.9405074e-04,\n",
              "        9.3541841e-04,  4.3088710e-03, -2.1798410e-03, -3.7200572e-03,\n",
              "        2.0141166e-04,  1.7118313e-03, -3.5897805e-04,  1.9944713e-03,\n",
              "       -2.6182244e-03,  3.3991842e-03, -4.2411317e-03,  3.2304187e-04,\n",
              "       -3.7947842e-03,  3.9213840e-03, -3.2863927e-03, -3.4094288e-03,\n",
              "       -5.0471826e-03,  3.7001725e-03,  5.2102907e-03, -3.2213696e-03,\n",
              "        3.4808710e-03,  2.1343965e-03,  1.5065513e-03,  1.3602152e-04,\n",
              "       -7.1587780e-04, -2.9562924e-03, -6.8885391e-04,  4.7557880e-03,\n",
              "       -7.2766544e-04, -5.3398809e-03,  1.5002950e-03, -1.0769188e-03,\n",
              "       -4.4552488e-03, -1.8676542e-04,  6.1743227e-03,  2.0808117e-03,\n",
              "       -1.9588359e-03, -3.9197593e-03,  2.2565117e-03, -3.2188366e-03,\n",
              "        1.9142843e-03,  3.7286866e-03,  4.8450073e-03, -2.9460408e-04,\n",
              "       -4.1756681e-03, -1.9259402e-03,  6.6710415e-04,  1.0056030e-03,\n",
              "        4.6407827e-03,  3.5958665e-03, -5.6955882e-04, -1.3128974e-03,\n",
              "       -4.3528000e-04,  3.7356216e-04, -8.5784144e-05, -7.4897142e-04,\n",
              "       -5.8804371e-04,  1.0508278e-03, -4.1348758e-04,  9.4454898e-04,\n",
              "       -2.4625219e-03,  3.2271703e-03,  1.9956212e-03, -2.2114536e-03,\n",
              "       -1.5811331e-04, -2.3457054e-03, -1.2642285e-03,  3.8411380e-03,\n",
              "       -2.9364012e-03,  5.8348215e-04, -4.3122699e-03, -7.1868836e-04,\n",
              "       -4.4714352e-03, -5.0916849e-04,  4.5596990e-03,  1.2881095e-03,\n",
              "        7.3809223e-04,  2.5204669e-03,  4.2326725e-03,  2.9272237e-03],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ih3kh3k9GvsY",
        "outputId": "a7b6480b-114e-4690-9ae3-9a7c336ca819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('family', 0.38792794942855835),\n",
              " ('video', 0.34598788619041443),\n",
              " ('water', 0.32853835821151733),\n",
              " ('reddit', 0.3031351566314697),\n",
              " ('release', 0.2986566424369812),\n",
              " ('northern', 0.2880091667175293),\n",
              " ('train', 0.2851136326789856),\n",
              " ('today', 0.2832205295562744),\n",
              " ('hiroshima', 0.27639293670654297),\n",
              " ('watch', 0.2743262052536011)]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'evacuation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjQdGoz5GvvF",
        "outputId": "8949343a-e7d0-4fb2-9b78-da494d00aed2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('obama', 0.340711385011673),\n",
              " ('hiroshima', 0.3070244789123535),\n",
              " ('atomic', 0.2936400771141052),\n",
              " ('release', 0.287919282913208),\n",
              " ('video', 0.27880674600601196),\n",
              " ('start', 0.2737729549407959),\n",
              " ('legionnaire', 0.2618742883205414),\n",
              " ('wreck', 0.25086820125579834),\n",
              " ('disaster', 0.24151623249053955),\n",
              " ('flood', 0.2403653860092163)]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'wildfire')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBrdMLgoGvx6",
        "outputId": "bc6e5fec-1c36-4fcc-91f9-c5b21a03b599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('bombing', 0.47136276960372925),\n",
              " ('california', 0.434434711933136),\n",
              " ('confirm', 0.422723650932312),\n",
              " ('obama', 0.4199334979057312),\n",
              " ('family', 0.412603497505188),\n",
              " ('release', 0.40194422006607056),\n",
              " ('legionnaire', 0.39913129806518555),\n",
              " ('hiroshima', 0.39819562435150146),\n",
              " ('bomber', 0.3954241871833801),\n",
              " ('northern', 0.38984280824661255)]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'disaster')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzT1ByONHBwJ",
        "outputId": "10ff7530-7e5b-46b3-b7e8-35f82a74dc99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('obama', 0.5804815292358398),\n",
              " ('video', 0.512363851070404),\n",
              " ('world', 0.476909875869751),\n",
              " ('northern', 0.47611120343208313),\n",
              " ('release', 0.4603372812271118),\n",
              " ('family', 0.45853543281555176),\n",
              " ('legionnaire', 0.4435676038265228),\n",
              " ('california', 0.440688818693161),\n",
              " ('suicide', 0.4124639630317688),\n",
              " ('reddit', 0.4028707444667816)]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'emergency')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vInBbIysHBzB",
        "outputId": "9a64eb40-9e6a-4968-b49e-239f2eb9223f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('update', 0.37865230441093445),\n",
              " ('wildfire', 0.2932456135749817),\n",
              " ('movie', 0.27414456009864807),\n",
              " ('truck', 0.26565542817115784),\n",
              " ('world', 0.26326608657836914),\n",
              " ('family', 0.25536999106407166),\n",
              " ('collapse', 0.24734605848789215),\n",
              " ('forest', 0.23598751425743103),\n",
              " ('bombing', 0.23524606227874756),\n",
              " ('great', 0.23261743783950806)]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar(model_sg, 'flood')"
      ],
      "metadata": {
        "id": "KwKtwsnWSCXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30bdb9e9-e00c-424e-d2aa-974992909bd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('bomber', 0.41440892219543457),\n",
              " ('family', 0.39707881212234497),\n",
              " ('wildfire', 0.3896235227584839),\n",
              " ('survive', 0.37608450651168823),\n",
              " ('wreck', 0.36468416452407837),\n",
              " ('legionnaire', 0.3580814301967621),\n",
              " ('fatal', 0.35789838433265686),\n",
              " ('people', 0.3576211929321289),\n",
              " ('bombing', 0.35669076442718506),\n",
              " ('quarantine', 0.34222763776779175)]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_sg, 'suicide', \"bomber\")"
      ],
      "metadata": {
        "id": "0VLNvIFzSEKk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8a62385-de69-4b19-f535-2ed1beb0c390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36694592"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_sg, 'california', \"wildfire\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFjDrDr6O4f9",
        "outputId": "647de83c-29e3-428f-f65f-9bd6b89b499a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.43443474"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similarity(model_sg, 'flood', \"disaster\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pVKSGxLPO41l",
        "outputId": "44922de7-f757-475c-a11f-6638d09438d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.31946275"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = model_sg.wv.vectors"
      ],
      "metadata": {
        "id": "DvCQmUFPnT0H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=2)\n",
        "result = pca.fit_transform(X)"
      ],
      "metadata": {
        "id": "dpc8JR9znY1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "plt.scatter(result[:, 0], result[:, 1])\n",
        "for i, word in enumerate(words):\n",
        "  plt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "MpsVXc4fnb9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "outputId": "68a5d743-1080-4890-cbf3-2e37f815ae20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Topic Modelling with BertTopic"
      ],
      "metadata": {
        "id": "20FZqUUnPdrJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf = TfidfVectorizer(ngram_range=(1,1),                       # n_gram_type\n",
        "                          token_pattern= r'([a-zA-Z0-9-/]{5,})', # num chars >= 5\n",
        "                          min_df=2).fit(df_3['text_cleaned'])    # minimum required occurences of a word      "
      ],
      "metadata": {
        "id": "BiQG7vJ3HS6V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BERTopic(nr_topics=10,\n",
        "    vectorizer_model=tfidf,\n",
        "    calculate_probabilities=True,\n",
        "    verbose=True)\n",
        "topics, probs = model.fit_transform(df_3['text_cleaned'])"
      ],
      "metadata": {
        "id": "PGTPs8n4HS9Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566,
          "referenced_widgets": [
            "22ea5ee7f7bf4125bcaf6211aa290949",
            "d1dc77c724af4e478d34b1408ad09326",
            "5b18f521a6c74e7db21c56fe05fac452",
            "3f3d3e8f8d33435d8ec7e8953f13f410",
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          "text": [
            "2023-01-30 01:05:11,499 - BERTopic - Transformed documents to Embeddings\n",
            "2023-01-30 01:05:51,358 - BERTopic - Reduced dimensionality\n",
            "2023-01-30 01:06:04,879 - BERTopic - Clustered reduced embeddings\n",
            "2023-01-30 01:06:12,536 - BERTopic - Reduced number of topics from 191 to 11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "freq = model.get_topic_info()\n",
        "freq.head(10)"
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        "colab": {
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          "height": 363
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      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "   Topic  Count                                   Name\n",
              "0     -1   5940       -1_nuclear_disaster_scream_wound\n",
              "1      0    224        0_siren_radio_aftershock_listen\n",
              "2      1    215         1_train_derail_fedex_bioterror\n",
              "3      2    197       2_crush_sinkhole_swallow_failure\n",
              "4      3    185        3_suicide_bomber_detonate_saudi\n",
              "5      4    160    4_accident_airplane_collision_space\n",
              "6      5    152  5_thunderstorm_severe_calgary_tornado\n",
              "7      6    146    6_evacuation_evacuate_order_abandon\n",
              "8      7    138   7_earthquake_seismic_tsunami_volcano\n",
              "9      8    134         8_collapse_bridge_nearby_blast"
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              "      <td>6_evacuation_evacuate_order_abandon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>138</td>\n",
              "      <td>7_earthquake_seismic_tsunami_volcano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>134</td>\n",
              "      <td>8_collapse_bridge_nearby_blast</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_topics()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdPm_SRmvD8P",
        "outputId": "423eaa82-8e38-441e-9ee8-45c57fa69543"
      },
      "execution_count": null,
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          "data": {
            "text/plain": [
              "{-1: [('nuclear', 0.028555768439274293),\n",
              "  ('disaster', 0.027232168927127),\n",
              "  ('scream', 0.023434910025916116),\n",
              "  ('wound', 0.022079648365253158),\n",
              "  ('weapon', 0.021734405728176285),\n",
              "  ('drown', 0.01998749544489667),\n",
              "  ('video', 0.019937531082822292),\n",
              "  ('electrocute', 0.019259924259217222),\n",
              "  ('family', 0.019006731267844697),\n",
              "  ('wreck', 0.018974561023072083)],\n",
              " 0: [('siren', 0.16402265489980467),\n",
              "  ('radio', 0.08443942501354565),\n",
              "  ('aftershock', 0.08324548809366533),\n",
              "  ('listen', 0.07906713166391098),\n",
              "  ('nowplaye', 0.06710963681557351),\n",
              "  ('dance', 0.06435265618102091),\n",
              "  ('music', 0.06095865930838312),\n",
              "  ('sleep', 0.051912665649042906),\n",
              "  ('soundcloud', 0.04223781139326311),\n",
              "  ('sandstorm', 0.04004319798391665)],\n",
              " 1: [('train', 0.24007037412736257),\n",
              "  ('derail', 0.18995611160041553),\n",
              "  ('fedex', 0.13665252332502276),\n",
              "  ('bioterror', 0.12761691676940215),\n",
              "  ('transport', 0.09710115843239497),\n",
              "  ('bioterrorism', 0.07273948823371863),\n",
              "  ('tragedy', 0.06943901212683005),\n",
              "  ('coach', 0.060338216842262565),\n",
              "  ('horror', 0.059845527090227016),\n",
              "  ('metro', 0.05844024812888834)],\n",
              " 2: [('crush', 0.18193552251744083),\n",
              "  ('sinkhole', 0.15825207645343545),\n",
              "  ('swallow', 0.14727178634168014),\n",
              "  ('failure', 0.1385228384827446),\n",
              "  ('structural', 0.13477862989625594),\n",
              "  ('airport', 0.11250287480976345),\n",
              "  ('sandstorm', 0.10185678729367165),\n",
              "  ('minute', 0.09913727759317993),\n",
              "  ('virgin', 0.09690387482361174),\n",
              "  ('investigator', 0.07636725875835143)],\n",
              " 3: [('suicide', 0.3084183815901169),\n",
              "  ('bomber', 0.22563718848793793),\n",
              "  ('detonate', 0.2097359986921108),\n",
              "  ('saudi', 0.16662671242140362),\n",
              "  ('turkey', 0.1176541313045399),\n",
              "  ('bombing', 0.11669994611162654),\n",
              "  ('trench', 0.10898423918147015),\n",
              "  ('release', 0.10439150039974011),\n",
              "  ('miner', 0.10353155389223866),\n",
              "  ('attack', 0.09901855071371922)],\n",
              " 4: [('accident', 0.21121864216841593),\n",
              "  ('airplane', 0.14350937697991545),\n",
              "  ('collision', 0.142840960732299),\n",
              "  ('space', 0.08341819939429006),\n",
              "  ('traffic', 0.08292350496057256),\n",
              "  ('involve', 0.07861471019650271),\n",
              "  ('crash', 0.07812476882571277),\n",
              "  ('fleet', 0.06856291975098006),\n",
              "  ('collide', 0.06609996700121823),\n",
              "  ('motorcyclist', 0.06260743887491269)],\n",
              " 5: [('thunderstorm', 0.23777925147268947),\n",
              "  ('severe', 0.1637529532831182),\n",
              "  ('calgary', 0.15453681226788918),\n",
              "  ('tornado', 0.1143131509827282),\n",
              "  ('warning', 0.10533448985454037),\n",
              "  ('hailstorm', 0.10227193649149492),\n",
              "  ('weather', 0.0883112310343453),\n",
              "  ('storm', 0.08624444646085265),\n",
              "  ('lightning', 0.0628777118374457),\n",
              "  ('issue', 0.058671434373659695)],\n",
              " 6: [('evacuation', 0.2543590756005664),\n",
              "  ('evacuate', 0.15468341581979783),\n",
              "  ('order', 0.13099131403524875),\n",
              "  ('abandon', 0.0708806006926045),\n",
              "  ('obliteration', 0.0679523808602775),\n",
              "  ('ancient', 0.06302809805603818),\n",
              "  ('emergency', 0.05031312486358396),\n",
              "  ('aircraft', 0.047066714542588456),\n",
              "  ('walmart', 0.041804820573236964),\n",
              "  ('prompt', 0.04094343977693174)],\n",
              " 7: [('earthquake', 0.2161899978519632),\n",
              "  ('seismic', 0.15502985850760476),\n",
              "  ('tsunami', 0.12829835700164482),\n",
              "  ('volcano', 0.12027161473451493),\n",
              "  ('landslide', 0.06506528584744363),\n",
              "  ('legacy', 0.06386216070781221),\n",
              "  ('intensity', 0.055807144699853255),\n",
              "  ('italian', 0.055589428718068976),\n",
              "  ('coast', 0.05452672714787867),\n",
              "  ('learn', 0.05376549156396009)],\n",
              " 8: [('collapse', 0.24910778095034117),\n",
              "  ('bridge', 0.20638367767129331),\n",
              "  ('nearby', 0.18250093738948786),\n",
              "  ('blast', 0.14853457500534664),\n",
              "  ('appear', 0.12820271187460774),\n",
              "  ('blight', 0.1051596123695535),\n",
              "  ('giant', 0.07737796140602567),\n",
              "  ('break', 0.06823771852115576),\n",
              "  ('broad', 0.06087677001652557),\n",
              "  ('australian', 0.06087677001652557)],\n",
              " 9: [('wildfire', 0.284084992758143),\n",
              "  ('california', 0.27536872346507246),\n",
              "  ('northern', 0.26136464655634073),\n",
              "  ('ablaze', 0.13055265826022472),\n",
              "  ('arson', 0.12168477785910824),\n",
              "  ('arsonist', 0.11723194770474094),\n",
              "  ('rocky', 0.09048708492821848),\n",
              "  ('catch', 0.06390157645701867),\n",
              "  ('suspect', 0.06187450923089676),\n",
              "  ('advance', 0.049968548519998025)]}"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_topic(9)"
      ],
      "metadata": {
        "id": "pJPazyWRIz4H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e164bfb-4879-4c75-c8a8-da678254beff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('wildfire', 0.284084992758143),\n",
              " ('california', 0.27536872346507246),\n",
              " ('northern', 0.26136464655634073),\n",
              " ('ablaze', 0.13055265826022472),\n",
              " ('arson', 0.12168477785910824),\n",
              " ('arsonist', 0.11723194770474094),\n",
              " ('rocky', 0.09048708492821848),\n",
              " ('catch', 0.06390157645701867),\n",
              " ('suspect', 0.06187450923089676),\n",
              " ('advance', 0.049968548519998025)]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.visualize_barchart()"
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      "metadata": {
        "id": "UlwOrPX6PuiV",
        "colab": {
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          "height": 517
        },
        "outputId": "846fae01-624e-4855-d35f-d4121f6dd583"
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      "execution_count": null,
      "outputs": [
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation BERTopic model \n",
        "# Preprocess documents\n",
        "cleaned_docs = model._preprocess_text(df_3['text_cleaned'])\n",
        "\n",
        "# Extract vectorizer and tokenizer from BERTopic\n",
        "vectorizer = model.vectorizer_model\n",
        "tokenizer = vectorizer.build_tokenizer()\n",
        "\n",
        "# Extract features for Topic Coherence evaluation\n",
        "words = vectorizer.get_feature_names()\n",
        "tokens = [tokenizer(doc) for doc in cleaned_docs]\n",
        "dictionary = corpora.Dictionary(tokens)\n",
        "corpus = [dictionary.doc2bow(token) for token in tokens]\n",
        "topic_words = [[words for words, _ in model.get_topic(topic)] \n",
        "               for topic in range(len(set(topics))-1)]\n",
        "\n",
        "# Evaluate\n",
        "coherence_model_bert = CoherenceModel(topics=topic_words, \n",
        "                                 texts=tokens, \n",
        "                                 corpus=corpus,\n",
        "                                 dictionary=dictionary, \n",
        "                                 coherence='u_mass')"
      ],
      "metadata": {
        "id": "vQ4wJBmnc_M8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# BERTopic coherence score\n",
        "coherence_bert = coherence_model_bert.get_coherence()\n",
        "print('\\nCoherence Score BERTopic: ', coherence_bert)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqQLuDwHc_VS",
        "outputId": "f12766c1-144e-403e-b312-7d751cf7ae05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Coherence Score BERTopic:  -13.968517251367896\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Topic Modelling with LDA"
      ],
      "metadata": {
        "id": "4PQ6ymZvsZfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Dictionary\n",
        "id2word = corpora.Dictionary(data)\n",
        "# Create Corpus\n",
        "texts = data\n",
        "# Term Document Frequency\n",
        "corpus = [id2word.doc2bow(text) for text in texts]"
      ],
      "metadata": {
        "id": "wyIomhQ8tMYG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build LDA model\n",
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n",
        "                                           id2word=id2word,\n",
        "                                           num_topics=10,\n",
        "                                           chunksize=200,  \n",
        "                                           random_state=0)"
      ],
      "metadata": {
        "id": "MgN6Lqe4zDBG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(lda_model.print_topics())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HofzofdFuA3L",
        "outputId": "812f9f90-58f5-4e25-cd21-fd93dbe68180"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0,\n",
            "  '0.035*\"security\" + 0.029*\"hawaii\" + 0.026*\"suffer\" + 0.019*\"soudelor\" + '\n",
            "  '0.018*\"earthquake\" + 0.017*\"today\" + 0.014*\"right\" + 0.012*\"turkey\" + '\n",
            "  '0.010*\"airport\" + 0.010*\"taiwan\"'),\n",
            " (1,\n",
            "  '0.052*\"police\" + 0.047*\"tornado\" + 0.025*\"people\" + 0.022*\"possible\" + '\n",
            "  '0.021*\"control\" + 0.019*\"threaten\" + 0.018*\"investigate\" + 0.018*\"ebike\" + '\n",
            "  '0.018*\"typhoon\" + 0.018*\"collided\"'),\n",
            " (2,\n",
            "  '0.027*\"little\" + 0.025*\"trauma\" + 0.024*\"ebola\" + 0.023*\"alabama\" + '\n",
            "  '0.019*\"change\" + 0.016*\"stock\" + 0.015*\"bombing\" + 0.014*\"richmond\" + '\n",
            "  '0.013*\"panic\" + 0.011*\"minute\"'),\n",
            " (3,\n",
            "  '0.035*\"world\" + 0.033*\"volcano\" + 0.030*\"siren\" + 0.028*\"warning\" + '\n",
            "  '0.026*\"survive\" + 0.022*\"watch\" + 0.020*\"project\" + 0.019*\"think\" + '\n",
            "  '0.017*\"terrorist\" + 0.016*\"walmart\"'),\n",
            " (4,\n",
            "  '0.049*\"suicide\" + 0.037*\"break\" + 0.032*\"violent\" + 0.030*\"storm\" + '\n",
            "  '0.026*\"saudi\" + 0.026*\"bomber\" + 0.025*\"mosque\" + 0.024*\"disaster\" + '\n",
            "  '0.017*\"obama\" + 0.017*\"thunder\"'),\n",
            " (5,\n",
            "  '0.037*\"scream\" + 0.026*\"train\" + 0.024*\"passenger\" + 0.017*\"miner\" + '\n",
            "  '0.011*\"tonight\" + 0.010*\"disney\" + 0.010*\"flood\" + 0.009*\"remember\" + '\n",
            "  '0.008*\"pretty\" + 0.008*\"murder\"'),\n",
            " (6,\n",
            "  '0.073*\"wound\" + 0.050*\"northern\" + 0.036*\"evacuate\" + 0.019*\"reuters\" + '\n",
            "  '0.018*\"trouble\" + 0.017*\"powerline\" + 0.015*\"great\" + 0.012*\"failure\" + '\n",
            "  '0.011*\"start\" + 0.011*\"glink\"'),\n",
            " (7,\n",
            "  '0.054*\"weapon\" + 0.031*\"video\" + 0.031*\"confirm\" + 0.028*\"state\" + '\n",
            "  '0.027*\"malaysia\" + 0.022*\"official\" + 0.020*\"nuclear\" + 0.018*\"record\" + '\n",
            "  '0.015*\"tsunami\" + 0.014*\"youtube\"'),\n",
            " (8,\n",
            "  '0.068*\"wreck\" + 0.046*\"california\" + 0.036*\"wildfire\" + 0.029*\"wreckage\" + '\n",
            "  '0.021*\"update\" + 0.019*\"officer\" + 0.018*\"investigator\" + 0.018*\"injury\" + '\n",
            "  '0.017*\"place\" + 0.016*\"order\"'),\n",
            " (9,\n",
            "  '0.039*\"family\" + 0.030*\"suspect\" + 0.028*\"spill\" + 0.025*\"release\" + '\n",
            "  '0.023*\"worldnew\" + 0.022*\"costlier\" + 0.022*\"refugio\" + 0.020*\"tragedy\" + '\n",
            "  '0.018*\"crash\" + 0.015*\"hollywood\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pprint(lda_model.print_topics()[8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YfEAhJtvN2w",
        "outputId": "31f68a40-780c-4c81-bc76-2b5613489aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8,\n",
            " '0.068*\"wreck\" + 0.046*\"california\" + 0.036*\"wildfire\" + 0.029*\"wreckage\" + '\n",
            " '0.021*\"update\" + 0.019*\"officer\" + 0.018*\"investigator\" + 0.018*\"injury\" + '\n",
            " '0.017*\"place\" + 0.016*\"order\"')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Evaluation LDA model \n",
        "coherence_model_lda = CoherenceModel(model=lda_model, texts=data, dictionary=id2word, coherence='u_mass')\n",
        "coherence_lda = coherence_model_lda.get_coherence()\n",
        "print('\\nCoherence Score LDA model: ', coherence_lda)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NraV4unMwNrX",
        "outputId": "56caf495-4c14-4319-ac3b-a85a1b00ec80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Coherence Score LDA model:  -18.755730701338212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# export data set\n",
        "#from google.colab import files\n",
        "#df_3.to_csv('df_train.csv')\n",
        "#files.download('df_train.csv') "
      ],
      "metadata": {
        "id": "4nFkCgVRvcIt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}