Text_Clustering_on_Hobbes's_Leviathan.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMsQqbBt+3vm3nUcz594Hom",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mehdimt1980/Text-Clustering/blob/main/Text_Clustering_on_Hobbes's_Leviathan.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "\n",
        "# Load the text data from the txt file and convert it into a Pandas dataframe\n",
        "with open('/content/leviathan.txt', 'r') as file:\n",
        "    data = file.readlines()\n",
        "\n",
        "df = pd.DataFrame(data, columns=['text'])\n",
        "\n",
        "# Create a TfidfVectorizer object to convert the text data into a matrix of TF-IDF values\n",
        "tfidf_vectorizer = TfidfVectorizer(stop_words='english')\n",
        "tfidf = tfidf_vectorizer.fit_transform(df['text'])\n",
        "\n",
        "# Determine the optimal number of clusters using the elbow method\n",
        "wcss = []\n",
        "for i in range(2, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', n_init='auto', random_state=42)\n",
        "    kmeans.fit(tfidf)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "plt.plot(range(2, 11), wcss)\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()\n",
        "\n",
        "# Fit a KMeans model with the optimal number of clusters determined from the elbow method\n",
        "num_clusters = 7 # optimal number of clusters determined from elbow method\n",
        "kmeans = KMeans(n_clusters=num_clusters, init='k-means++', n_init='auto', random_state=42)\n",
        "kmeans.fit(tfidf)\n",
        "\n",
        "# Get the top clusters and their corresponding words\n",
        "order_centroids = kmeans.cluster_centers_.argsort()[:, ::-1]\n",
        "terms = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "top_clusters = {}\n",
        "for i in range(num_clusters):\n",
        "    cluster_num = i\n",
        "    words = [terms[ind] for ind in order_centroids[i, :10]]\n",
        "    top_clusters[cluster_num] = words\n",
        "\n",
        "# Visualize the top clusters and their words\n",
        "for cluster_num, words in top_clusters.items():\n",
        "    print(f'Cluster {cluster_num}: {\" | \".join(words)}')\n",
        "\n",
        "# Visualize the hierarchical distances of each cluster using a dendrogram\n",
        "Z = linkage(kmeans.cluster_centers_, 'ward')\n",
        "plt.figure(figsize=(10, 5))\n",
        "dendrogram(Z)\n",
        "plt.title('Dendrogram')\n",
        "plt.ylabel('Distance')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QEgPcRJUrlM2",
        "outputId": "6c8ca9ac-d7ce-471f-b5e3-58758e75d62c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster 0: regeneration | reigning | followed | son | called | preparation | representeth | properly | yee | teaching\n",
            "Cluster 1: condensed | rarefied | seen | lesse | time | say | greater | event | eve | evangile\n",
            "Cluster 2: god | man | men | power | law | soveraign | shall | commonwealth | say | hath\n",
            "Cluster 3: quick | bitten | democraticall | entrance | cease | permanent | refuse | strong | dead | monarchy\n",
            "Cluster 4: thing | man | right | men | hath | nature | selfe | say | contrary | body\n",
            "Cluster 5: covenants | voyd | nature | mutuall | author | men | invalid | performance | trust | soveraignty\n",
            "Cluster 6: act | punishment | hostility | soveraign | voluntary | assembly | present | power | onely | proceed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}