topic_modeling_comparison.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/efeecllk/investor-sentiments/blob/main/topic_modeling_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TKE-JP6fsZGt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9548b568-cc2b-40c1-dd40-ea192d83f8df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting emoji\n",
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            "\u001b[?25hInstalling collected packages: emoji\n",
            "Successfully installed emoji-2.14.0\n"
          ]
        }
      ],
      "source": [
        "!pip install emoji\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCOSkkx-HJMs",
        "outputId": "0a912277-b888-4388-b8be-1016b08299b8",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install datasets scikit-learn pyLDAvis nltk\n",
        "!pip install --upgrade datasets\n",
        "!pip install pandas scikit-learn pyLDAvis"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime, timedelta\n",
        "import matplotlib.pyplot as plt\n",
        "import gensim\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import pickle\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import spacy\n",
        "import string\n",
        "import re\n",
        "import emoji"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ughhdoa9ivnE",
        "outputId": "8566c9e0-079d-4ada-dd90-607e13350650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sI_Jr1XpdXgk",
        "outputId": "bb75f6f8-3fcb-4592-e6ca-a45ac53092d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.decomposition import LatentDirichletAllocation"
      ],
      "metadata": {
        "id": "s3T-UHvghN4d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dnnu3WYaHT7W",
        "outputId": "e7b044e9-d413-420b-dd96-17029cbefd69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "artificial_df = pd.read_csv('/content/drive/MyDrive/NLP/clean_tweet.csv')\n",
        "real_df = pd.read_csv('/content/drive/MyDrive/NLP/Bitcoin_tweets.csv', nrows=10000)\n",
        "real_df = real_df[['text']]\n",
        "\n",
        "df_label_0 = artificial_df[artificial_df['label'] == 0].sample(n=5000, random_state=42)\n",
        "df_label_1 = artificial_df[artificial_df['label'] == 1].sample(n=5000, random_state=42)\n",
        "df_label_2 = artificial_df[artificial_df['label'] == 2].sample(n=5000, random_state=42)\n",
        "\n",
        "artificial_df = pd.concat([df_label_0, df_label_1, df_label_2]).reset_index(drop=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "1yceQhCwfevh",
        "outputId": "8ee94b50-570f-4846-c1be-4ce2682d0c39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                                                text\n",
              "0  Blue Ridge Bank shares halted by NYSE after #b...\n",
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          "execution_count": 33
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        "real_df.head()"
      ]
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    {
      "cell_type": "code",
      "source": [
        "artificial_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
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        "id": "cR4F6ZJmiYR6",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
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              "                                              text_v  label\n",
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              "\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-2db4be46-0070-420f-8127-f1bb8b0743b1 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-2db4be46-0070-420f-8127-f1bb8b0743b1');\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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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d162a420-6614-4638-8ec7-869ce921a0ac')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d162a420-6614-4638-8ec7-869ce921a0ac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "artificial_df",
              "summary": "{\n  \"name\": \"artificial_df\",\n  \"rows\": 15000,\n  \"fields\": [\n    {\n      \"column\": \"text_v\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 14996,\n        \"samples\": [\n          \"hat sucker sold loss eeenf stockmarket\",\n          \"financial market buzzing excitement innovative startup enter scene expanding opportunity investor\",\n          \"xrp poised skyrocket year xrpthestandard ripple\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_text(text):\n",
        "    text = emoji.demojize(text)\n",
        "    text = text.lower()\n",
        "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
        "    text = re.sub(r'\\d+', '', text)\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    text = ' '.join([word for word in word_tokenize(text) if word not in stop_words])\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    text = ' '.join([lemmatizer.lemmatize(word) for word in word_tokenize(text)])\n",
        "    return text\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtTuqkTuiagF",
        "outputId": "677a69ae-0b36-41a4-badc-282f7390b7f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('punkt_tab')\n",
        "nltk.download('wordnet')\n",
        "real_df['text'] = real_df['text'].apply(preprocess_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jao3aA71iiNS",
        "outputId": "68b58592-fd3c-4b08-d258-4caa14cc17fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Package punkt_tab is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "\n",
        "# artificial data\n",
        "vectorizer_artificial = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')\n",
        "dtm_artificial = vectorizer_artificial.fit_transform(artificial_df['text_v'])\n",
        "\n",
        "lda_artificial = LatentDirichletAllocation(n_components=5, random_state=42)\n",
        "lda_artificial.fit(dtm_artificial)\n",
        "\n",
        "def print_topics(model, vectorizer, top_n=10):\n",
        "    topics = {}\n",
        "    for idx, topic in enumerate(model.components_):\n",
        "        topics[f\"Topic {idx+1}\"] = [vectorizer.get_feature_names_out()[i] for i in topic.argsort()[-top_n:]]\n",
        "    return topics\n",
        "\n",
        "artificial_topics = print_topics(lda_artificial, vectorizer_artificial)\n",
        "print(\"\\nArtificial Data Topics:\")\n",
        "for topic, words in artificial_topics.items():\n",
        "    print(f\"{topic}: {', '.join(words)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGkjqU6PjMn4",
        "outputId": "38c350be-63fa-4fc4-e1c2-86bc6fc40384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Artificial Data Topics:\n",
            "Topic 1: eth, high, dont, btc, stock, time, rocket, new, market, crypto\n",
            "Topic 2: expert, analyst, crypto, growth, economic, stock, global, investor, financial, market\n",
            "Topic 3: news, major, new, latest, share, industry, financial, earnings, company, report\n",
            "Topic 4: potential, im, trading, portfolio, investor, investing, financial, stock, investment, market\n",
            "Topic 5: potential, strong, im, tech, analyst, chartincreasing, aapl, bullish, price, stock\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# real data\n",
        "\n",
        "# Vectorize\n",
        "vectorizer_real = CountVectorizer(max_df=0.95, min_df=2, stop_words='english')\n",
        "dtm_real = vectorizer_real.fit_transform(real_df['text'])\n",
        "\n",
        "# Apply LDA\n",
        "lda_real = LatentDirichletAllocation(n_components=5, random_state=42)\n",
        "lda_real.fit(dtm_real)\n",
        "\n",
        "# Extract topics\n",
        "real_topics = print_topics(lda_real, vectorizer_real)\n",
        "print(\"\\nReal Data Topics:\")\n",
        "for topic, words in real_topics.items():\n",
        "    print(f\"{topic}: {', '.join(words)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvodCZOijrpz",
        "outputId": "6cb07684-33a4-42a9-b6cd-cb83499ba641"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Real Data Topics:\n",
            "Topic 1: crypto, hour, btcusd, update, ethereum, price, xrp, bitcoin, eth, btc\n",
            "Topic 2: freebitcoin, binance, bitcoinmining, dogecoin, doge, tesla, crypto, elonmusk, btc, bitcoin\n",
            "Topic 3: day, free, crypto, price, buy, usd, target, new, btc, bitcoin\n",
            "Topic 4: igaming, betfuryio, handshake, wi, exciting, experience, platform, follow, bitcoins, let\n",
            "Topic 5: high, best, hour, cryptocurrency, market, tesla, price, crypto, btc, bitcoin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare Artificial and Real Topics\n",
        "\n",
        "def compare_topics(artificial_topics, real_topics):\n",
        "    comparison = {}\n",
        "    for a_topic, a_words in artificial_topics.items():\n",
        "        for r_topic, r_words in real_topics.items():\n",
        "            common_words = set(a_words).intersection(set(r_words))\n",
        "            comparison[(a_topic, r_topic)] = len(common_words)\n",
        "    return comparison\n",
        "\n",
        "topic_comparison = compare_topics(artificial_topics, real_topics)\n",
        "\n",
        "# Display comparison\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "comparison_df = pd.DataFrame([\n",
        "    {\"Artificial Topic\": k[0], \"Real Topic\": k[1], \"Common Words\": v}\n",
        "    for k, v in topic_comparison.items()\n",
        "])\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(\n",
        " comparison_df.pivot(index=\"Artificial Topic\", columns=\"Real Topic\", values=\"Common Words\"),\n",
        " annot=True, cmap=\"YlGnBu\"\n",
        ")\n",
        "plt.title('Topic Similarity Between Artificial and Real Data')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 773
        },
        "id": "mD6Dxuyuj5hi",
        "outputId": "33d4a546-281d-474e-efe7-b4f244006ca8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 5: Visualize Topic Distribution\n",
        "\n",
        "# Artificial Topic Distribution\n",
        "artificial_topic_distribution = np.argmax(lda_artificial.transform(dtm_artificial), axis=1)\n",
        "real_topic_distribution = np.argmax(lda_real.transform(dtm_real), axis=1)\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "sns.countplot(x=artificial_topic_distribution)\n",
        "plt.title('Artificial Data Topic Distribution')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "sns.countplot(x=real_topic_distribution)\n",
        "plt.title('Real Data Topic Distribution')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "oBBybFT8kMI7",
        "outputId": "4e16f290-151f-4ec0-d2fe-784ef01eae16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analyze Topic Differences\n",
        "\n",
        "def analyze_topic_differences(artificial_topics, real_topics, top_n=5):\n",
        "    from IPython.display import display, HTML\n",
        "\n",
        "    results = []\n",
        "    for a_topic, a_words in artificial_topics.items():\n",
        "        for r_topic, r_words in real_topics.items():\n",
        "            common_words = set(a_words).intersection(set(r_words))\n",
        "            overlap_percentage = (len(common_words) / top_n) * 100  # Benzerlik yüzdesi\n",
        "\n",
        "            results.append({\n",
        "                'Artificial Topic': a_topic,\n",
        "                'Real Topic': r_topic,\n",
        "                'Common Words': ', '.join(common_words) if common_words else 'None',\n",
        "                'Overlap (%)': round(overlap_percentage, 2)\n",
        "            })\n",
        "\n",
        "    # Create DataFrame for structured display\n",
        "    comparison_df = pd.DataFrame(results)\n",
        "    comparison_df = comparison_df.sort_values(by='Overlap (%)', ascending=False).reset_index(drop=True)\n",
        "\n",
        "    # Display the results in a styled table\n",
        "    display(HTML(comparison_df.to_html(index=False)))\n",
        "\n",
        "    # Highlight key observations\n",
        "    print(\"\\n🔍 **Key Observations:**\")\n",
        "    top_matches = comparison_df[comparison_df['Overlap (%)'] > 30]  # %30'dan fazla benzerlik olanlar\n",
        "\n",
        "# Call the function\n",
        "analyze_topic_differences(artificial_topics, real_topics)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "KQFuycHQkTp8",
        "outputId": "707c8973-c5de-4f1c-f818-49880b9a8a4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Artificial Topic</th>\n",
              "      <th>Real Topic</th>\n",
              "      <th>Common Words</th>\n",
              "      <th>Overlap (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>Topic 1</td>\n",
              "      <td>Topic 5</td>\n",
              "      <td>high, btc, crypto, market</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 1</td>\n",
              "      <td>Topic 1</td>\n",
              "      <td>eth, btc, crypto</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 1</td>\n",
              "      <td>Topic 3</td>\n",
              "      <td>new, btc, crypto</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 2</td>\n",
              "      <td>Topic 5</td>\n",
              "      <td>crypto, market</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 1</td>\n",
              "      <td>Topic 2</td>\n",
              "      <td>btc, crypto</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 5</td>\n",
              "      <td>Topic 3</td>\n",
              "      <td>price</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 5</td>\n",
              "      <td>Topic 1</td>\n",
              "      <td>price</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 4</td>\n",
              "      <td>Topic 5</td>\n",
              "      <td>market</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 3</td>\n",
              "      <td>Topic 3</td>\n",
              "      <td>new</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 2</td>\n",
              "      <td>Topic 3</td>\n",
              "      <td>crypto</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 2</td>\n",
              "      <td>Topic 2</td>\n",
              "      <td>crypto</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 2</td>\n",
              "      <td>Topic 1</td>\n",
              "      <td>crypto</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 5</td>\n",
              "      <td>Topic 5</td>\n",
              "      <td>price</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 2</td>\n",
              "      <td>Topic 4</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 3</td>\n",
              "      <td>Topic 1</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 3</td>\n",
              "      <td>Topic 2</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 3</td>\n",
              "      <td>Topic 4</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 3</td>\n",
              "      <td>Topic 5</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 4</td>\n",
              "      <td>Topic 1</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 4</td>\n",
              "      <td>Topic 2</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 4</td>\n",
              "      <td>Topic 3</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 4</td>\n",
              "      <td>Topic 4</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 5</td>\n",
              "      <td>Topic 2</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 1</td>\n",
              "      <td>Topic 4</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>Topic 5</td>\n",
              "      <td>Topic 4</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔍 **Key Observations:**\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat_minor": 0
}