Topic_Modelling_LDA.ipynb

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  "metadata": {
    "colab": {
      "name": "Topic_Modelling_LDA.ipynb",
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      "include_colab_link": true
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      "language": "python",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rishuatgithub/MLPy/blob/master/Topic_Modelling_LDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Qw-7mbkMg9FK"
      },
      "source": [
        "# Topic Modelling using Latent-Dirichlet Allocation\n",
        "\n",
        "- Blog URL : [Topic Modelling : Latent Dirichlet Allocation, an introduction](https://anotherreeshu.wordpress.com)\n",
        "- Author   : Rishu Shrivastava"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C1UbX8SHg7sh",
        "colab": {}
      },
      "source": [
        "## required installation for LDA visualization\n",
        "!pip install pyLDAvis\n",
        "\n",
        "## imports\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import pyLDAvis\n",
        "from pyLDAvis import sklearn\n",
        "pyLDAvis.enable_notebook()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rQDt-6pYwVoN"
      },
      "source": [
        "### Step 1: Loading and Understanding Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYUCw_hPxc2r",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"active active-primary\">\n",
        "As part of this step we will load an existing dataset and load it into a pandas dataframe. We will also try to on a brief understand the data.\n",
        "\n",
        "- Source of the dataset is from Kaggle [News category classifer](https://www.kaggle.com/hengzheng/news-category-classifier-val-acc-0-65).</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jUeRe8UkhNSZ",
        "outputId": "5e23e0e6-665c-4255-a1aa-8f1c1dfd619d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "### Reading the dataset from path\n",
        "\n",
        "filename = 'News_Category_Dataset_v2.json'\n",
        "data = pd.read_json(filename, lines=True)\n",
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>headline</th>\n",
              "      <th>authors</th>\n",
              "      <th>link</th>\n",
              "      <th>short_description</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CRIME</td>\n",
              "      <td>There Were 2 Mass Shootings In Texas Last Week...</td>\n",
              "      <td>Melissa Jeltsen</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/texas-ama...</td>\n",
              "      <td>She left her husband. He killed their children...</td>\n",
              "      <td>2018-05-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Will Smith Joins Diplo And Nicky Jam For The 2...</td>\n",
              "      <td>Andy McDonald</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/will-smit...</td>\n",
              "      <td>Of course it has a song.</td>\n",
              "      <td>2018-05-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Hugh Grant Marries For The First Time At Age 57</td>\n",
              "      <td>Ron Dicker</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/hugh-gran...</td>\n",
              "      <td>The actor and his longtime girlfriend Anna Ebe...</td>\n",
              "      <td>2018-05-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Jim Carrey Blasts 'Castrato' Adam Schiff And D...</td>\n",
              "      <td>Ron Dicker</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/jim-carre...</td>\n",
              "      <td>The actor gives Dems an ass-kicking for not fi...</td>\n",
              "      <td>2018-05-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Julianna Margulies Uses Donald Trump Poop Bags...</td>\n",
              "      <td>Ron Dicker</td>\n",
              "      <td>https://www.huffingtonpost.com/entry/julianna-...</td>\n",
              "      <td>The \"Dietland\" actress said using the bags is ...</td>\n",
              "      <td>2018-05-26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        category                                           headline  \\\n",
              "0          CRIME  There Were 2 Mass Shootings In Texas Last Week...   \n",
              "1  ENTERTAINMENT  Will Smith Joins Diplo And Nicky Jam For The 2...   \n",
              "2  ENTERTAINMENT    Hugh Grant Marries For The First Time At Age 57   \n",
              "3  ENTERTAINMENT  Jim Carrey Blasts 'Castrato' Adam Schiff And D...   \n",
              "4  ENTERTAINMENT  Julianna Margulies Uses Donald Trump Poop Bags...   \n",
              "\n",
              "           authors                                               link  \\\n",
              "0  Melissa Jeltsen  https://www.huffingtonpost.com/entry/texas-ama...   \n",
              "1    Andy McDonald  https://www.huffingtonpost.com/entry/will-smit...   \n",
              "2       Ron Dicker  https://www.huffingtonpost.com/entry/hugh-gran...   \n",
              "3       Ron Dicker  https://www.huffingtonpost.com/entry/jim-carre...   \n",
              "4       Ron Dicker  https://www.huffingtonpost.com/entry/julianna-...   \n",
              "\n",
              "                                   short_description       date  \n",
              "0  She left her husband. He killed their children... 2018-05-26  \n",
              "1                           Of course it has a song. 2018-05-26  \n",
              "2  The actor and his longtime girlfriend Anna Ebe... 2018-05-26  \n",
              "3  The actor gives Dems an ass-kicking for not fi... 2018-05-26  \n",
              "4  The \"Dietland\" actress said using the bags is ... 2018-05-26  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7nABVK_Omv3q",
        "outputId": "f2b9babf-dc0e-45ed-ebb7-b4ac58b2beb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "### data dimensions (rows, columns) of the dataset we are dealing with\n",
        "\n",
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200853, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2mcwlkrPm6Oo",
        "outputId": "d5429d11-b38f-4025-805a-8312a1ea9d91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        }
      },
      "source": [
        "### Total articles by category spread - viz\n",
        "\n",
        "plt.figure(figsize=(20,5))\n",
        "sns.set_style(\"whitegrid\")\n",
        "sns.countplot(x='category',data=data, orient='h', palette='husl')\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Category count of article\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-7yH62xxc22",
        "colab_type": "text"
      },
      "source": [
        "<div class =\"alert alert-success\">As we can see in the above diagram, a lot of new relates to Politics and its related items. Also we can understand a total of 20 new categories are defined in this dataset. So as part of Topic modelling exercise we can try to categories the dataset into 20 topics.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Baz_eBiCxc23",
        "colab_type": "text"
      },
      "source": [
        "### Step 2: Transforming the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6U8aunHEwN7y"
      },
      "source": [
        "<div class=\"alert alert-warning\">For the purpose of this demo and blog, we will do the following:\n",
        "\n",
        "  1.   **Combine** both the **Headline and Short Description** into one single column to bring more context to the news and corpus. Calling it as: ```Combined_Description```\n",
        "  2.  **Drop** rest of the attributes from the dataframe other than Combined_Description and Categories.\n",
        "    \n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-W3lHt3-vON1",
        "outputId": "581595c9-02aa-4169-c684-dd4eab49cbc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "### tranform the dataset to fit the original requirement\n",
        "\n",
        "data['Combined_Description'] = data['headline'] + data['short_description']\n",
        "\n",
        "filtered_data = data[['category','Combined_Description']]\n",
        "\n",
        "filtered_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>Combined_Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CRIME</td>\n",
              "      <td>There Were 2 Mass Shootings In Texas Last Week...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Will Smith Joins Diplo And Nicky Jam For The 2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Hugh Grant Marries For The First Time At Age 5...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Jim Carrey Blasts 'Castrato' Adam Schiff And D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Julianna Margulies Uses Donald Trump Poop Bags...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        category                               Combined_Description\n",
              "0          CRIME  There Were 2 Mass Shootings In Texas Last Week...\n",
              "1  ENTERTAINMENT  Will Smith Joins Diplo And Nicky Jam For The 2...\n",
              "2  ENTERTAINMENT  Hugh Grant Marries For The First Time At Age 5...\n",
              "3  ENTERTAINMENT  Jim Carrey Blasts 'Castrato' Adam Schiff And D...\n",
              "4  ENTERTAINMENT  Julianna Margulies Uses Donald Trump Poop Bags..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PrNM1Y8kBhZZ",
        "outputId": "37268b60-cf1c-4ff5-ff90-b7a4c4e260e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "## checking the dimensions of filtered data\n",
        "filtered_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200853, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5CeNOj-U5ka-"
      },
      "source": [
        "<div class=\"alert alert-warning\">\n",
        "    <b>Applying TFIDFVectorizer to pre-process the data into vectors.</b>\n",
        "\n",
        "- max_df : Ignore the words that occurs more than 95% of the corpus. \n",
        "- min_df : Accept the words in preparation of vocab that occurs in atleast 2 of the documents in the corpus.\n",
        "- stop_words : Remove the stop words. We can do this in separate steps or in a single step.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oMviYF-T5QLw",
        "colab": {}
      },
      "source": [
        "df_tfidf = TfidfVectorizer(max_df=0.5, min_df=10, stop_words='english', lowercase=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVNAlqdJ6or7",
        "colab": {}
      },
      "source": [
        "df_tfidf_transformed = df_tfidf.fit_transform(filtered_data['Combined_Description'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AO91FbiIA8x0",
        "outputId": "9c51dda2-4b0a-4d83-9deb-b6666ffa5844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "df_tfidf_transformed"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<200853x21893 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 2740307 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FjqPYCIuB-si"
      },
      "source": [
        "<div class=\"alert alert-success\"> \n",
        "Here you can notice that the transformed dataset holds a sparse matrix with a dimension of 200853x21893; where 200853 is the total number of rows and 21893 is the total word corpus.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "g-GkVdCHCqo-"
      },
      "source": [
        "### Step 3: Building Latent-Dirichlet Algorithm using scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "h33cgl50BFgX",
        "colab": {}
      },
      "source": [
        "### Define the LDA model and set the topic size to 20.\n",
        "\n",
        "topic_clusters = 20\n",
        "\n",
        "lda_model = LatentDirichletAllocation(n_components=topic_clusters, batch_size=128, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lr_ESU3ODNaH",
        "outputId": "9b033c0e-4ed0-484f-f84b-aedddc2b24fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "### Fit the filtered data to the model\n",
        "\n",
        "lda_model.fit(df_tfidf_transformed)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
              "                          evaluate_every=-1, learning_decay=0.7,\n",
              "                          learning_method='batch', learning_offset=10.0,\n",
              "                          max_doc_update_iter=100, max_iter=10,\n",
              "                          mean_change_tol=0.001, n_components=20, n_jobs=None,\n",
              "                          perp_tol=0.1, random_state=42, topic_word_prior=None,\n",
              "                          total_samples=1000000.0, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VcKrCkExc3M",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-danger\">\n",
        "Note: Fitting the model to the dataset take a long time. You will see the output as model summary, if success.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qOrdPjfnFhia"
      },
      "source": [
        "### Step 4: LDA Topic Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LyxCeru0DTNh",
        "outputId": "fa156de0-500c-41b1-f9db-5f7d67528a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "topic_word_dict = {}\n",
        "\n",
        "top_n_words_num = 10\n",
        "\n",
        "for index, topic in enumerate(lda_model.components_):\n",
        "  topic_id = index\n",
        "  topic_words_max = [df_tfidf.get_feature_names()[i] for i in topic.argsort()[-top_n_words_num:]]\n",
        "  topic_word_dict[topic_id] = topic_words_max\n",
        "\n",
        "  print(f\"Topic ID : {topic_id}; Top 10 Most Words : {topic_words_max}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic ID : 0; Top 10 Most Words : ['gun', 'police', 'violence', 'health', 'people', 'school', 'new', 'study', 'sexual', 'women']\n",
            "Topic ID : 1; Top 10 Most Words : ['stewart', 'flint', 'rico', 'storm', 'climate', 'new', 'jon', 'puerto', 'hurricane', 'water']\n",
            "Topic ID : 2; Top 10 Most Words : ['crash', 'police', 'airlines', 'cup', 'driver', 'coffee', 'colbert', 'man', 'stephen', 'car']\n",
            "Topic ID : 3; Top 10 Most Words : ['new', 'photo', 'hair', 'week', 'look', 'dress', 'style', 'wedding', 'fashion', 'photos']\n",
            "Topic ID : 4; Top 10 Most Words : ['years', 'players', 'saudi', 'nfl', 'old', 'man', 'football', 'year', 'francis', 'pope']\n",
            "Topic ID : 5; Top 10 Most Words : ['day', 'love', 'make', 'don', 'need', 'things', 'know', 'people', 'time', 'life']\n",
            "Topic ID : 6; Top 10 Most Words : ['dead', 'olympic', 'killed', 'drag', 'israel', 'year', 'festival', 'new', 'police', 'film']\n",
            "Topic ID : 7; Top 10 Most Words : ['movie', 'year', 'williams', 'trailer', 'isis', 'jennifer', 'netflix', 'new', 'wars', 'star']\n",
            "Topic ID : 8; Top 10 Most Words : ['day', 'students', 'parents', 'dads', 'week', 'moms', 'women', 'tweets', 'funniest', 'college']\n",
            "Topic ID : 9; Top 10 Most Words : ['kendall', 'new', 'tom', 'kylie', 'amy', 'west', 'kanye', 'kim', 'jenner', 'kardashian']\n",
            "Topic ID : 10; Top 10 Most Words : ['man', 'alabama', 'halloween', 'box', 'kimmel', 'office', 'moore', 'trump', 'fallon', 'jimmy']\n",
            "Topic ID : 11; Top 10 Most Words : ['game', 'james', 'president', 'obama', 'bernie', 'donald', 'sanders', 'trump', 'hillary', 'clinton']\n",
            "Topic ID : 12; Top 10 Most Words : ['10', 'year', 'new', 'tips', 'family', 'best', 'day', 'time', 'holiday', 'travel']\n",
            "Topic ID : 13; Top 10 Most Words : ['ban', 'judge', 'white', 'news', 'hill', 'president', 'donald', 'supreme', 'court', 'trump']\n",
            "Topic ID : 14; Top 10 Most Words : ['like', 'best', 'dogs', 'city', 'new', 'world', 'dog', 'guide', 'photos', 'gps']\n",
            "Topic ID : 15; Top 10 Most Words : ['chocolate', 'photos', 'foods', 'day', 'best', 'recipe', 'eat', 'make', 'recipes', 'food']\n",
            "Topic ID : 16; Top 10 Most Words : ['style', 'photos', 'sure', 'tumblr', 'instagram', 'huffpost', 'pinterest', 'check', 'twitter', 'facebook']\n",
            "Topic ID : 17; Top 10 Most Words : ['disease', 'loss', 'risk', 'pounds', 'study', 'women', 'ebola', 'cancer', 'lost', 'weight']\n",
            "Topic ID : 18; Top 10 Most Words : ['election', 'new', 'republicans', 'house', 'republican', 'obama', 'president', 'gop', 'donald', 'trump']\n",
            "Topic ID : 19; Top 10 Most Words : ['music', 'home', 'video', 'best', 'city', 'hotel', 'world', 'art', 'new', 'photos']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EJ-oXnm4G14W"
      },
      "source": [
        "<div class=\"alert alert-warning\">\n",
        "Transforming the existing dataframe and adding the content with a topic id and LDA generated topics\n",
        "<div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AjUhh31aGTtq",
        "colab": {}
      },
      "source": [
        "topic_output = lda_model.transform(df_tfidf_transformed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BuY5CvjAHyhC",
        "colab": {}
      },
      "source": [
        "filtered_data = filtered_data.copy()\n",
        "\n",
        "filtered_data['LDA_Topic_ID'] = topic_output.argmax(axis=1)\n",
        "\n",
        "filtered_data['Topic_word_categories'] = filtered_data['LDA_Topic_ID'].apply(lambda id: topic_word_dict[id])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CyX8CBlaIJFg",
        "outputId": "7a1817a2-eddd-4084-a617-689abd7a9bfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "filtered_data[['category','Combined_Description','LDA_Topic_ID','Topic_word_categories']].head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>Combined_Description</th>\n",
              "      <th>LDA_Topic_ID</th>\n",
              "      <th>Topic_word_categories</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CRIME</td>\n",
              "      <td>There Were 2 Mass Shootings In Texas Last Week...</td>\n",
              "      <td>0</td>\n",
              "      <td>[gun, police, violence, health, people, school...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Will Smith Joins Diplo And Nicky Jam For The 2...</td>\n",
              "      <td>2</td>\n",
              "      <td>[crash, police, airlines, cup, driver, coffee,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Hugh Grant Marries For The First Time At Age 5...</td>\n",
              "      <td>7</td>\n",
              "      <td>[movie, year, williams, trailer, isis, jennife...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Jim Carrey Blasts 'Castrato' Adam Schiff And D...</td>\n",
              "      <td>13</td>\n",
              "      <td>[ban, judge, white, news, hill, president, don...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENTERTAINMENT</td>\n",
              "      <td>Julianna Margulies Uses Donald Trump Poop Bags...</td>\n",
              "      <td>13</td>\n",
              "      <td>[ban, judge, white, news, hill, president, don...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        category                               Combined_Description  \\\n",
              "0          CRIME  There Were 2 Mass Shootings In Texas Last Week...   \n",
              "1  ENTERTAINMENT  Will Smith Joins Diplo And Nicky Jam For The 2...   \n",
              "2  ENTERTAINMENT  Hugh Grant Marries For The First Time At Age 5...   \n",
              "3  ENTERTAINMENT  Jim Carrey Blasts 'Castrato' Adam Schiff And D...   \n",
              "4  ENTERTAINMENT  Julianna Margulies Uses Donald Trump Poop Bags...   \n",
              "\n",
              "   LDA_Topic_ID                              Topic_word_categories  \n",
              "0             0  [gun, police, violence, health, people, school...  \n",
              "1             2  [crash, police, airlines, cup, driver, coffee,...  \n",
              "2             7  [movie, year, williams, trailer, isis, jennife...  \n",
              "3            13  [ban, judge, white, news, hill, president, don...  \n",
              "4            13  [ban, judge, white, news, hill, president, don...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yiyop4tqRh7n"
      },
      "source": [
        "### Step 6: Visualizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lvFrlzj1dEkX",
        "colab": {}
      },
      "source": [
        "viz = sklearn.prepare(lda_model=lda_model, dtm=df_tfidf_transformed, vectorizer=df_tfidf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Dap5ahJYf0vk",
        "outputId": "decf49ea-3121-40b7-bc20-bd45af1ff741",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        }
      },
      "source": [
        "pyLDAvis.display(viz)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el5738348092148808966722932\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el5738348092148808966722932_data = {\"mdsDat\": {\"x\": [-0.029714569815474046, 0.1763898958054039, 0.1159866618921345, -0.10815655585720699, -0.17468465048850085, -0.1609352229152136, -0.08106870810514546, -0.10268031130597166, -0.005588255082432565, 0.02044489892467586, -0.12623447584750597, 0.14690964066119655, 0.03568721120853407, 0.020768987673364617, 0.06119777667091056, 0.11457887337286804, 0.0064047774820598005, 0.028114280133843428, 0.01480380606583873, 0.04777593952662037], \"y\": [0.15993747962332966, 0.05208946044423438, 0.09428609931775572, -0.053659703315229214, -0.0825992111540832, 0.14029032273692918, 0.10837281368103237, 0.06368878510716687, -0.07344357032258395, -0.0807462825207155, -0.11255319758727655, -0.027084937596476007, -0.11400418763747566, -0.06179523294669549, -0.06989816428470304, -0.013339784370364165, 0.027087196369324735, 0.0822734157051292, -0.11647010605351153, 0.07756880480421165], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [13.450921322638948, 11.638615880999588, 7.423431352794281, 5.977779493204665, 5.805430052473812, 4.884226807105186, 4.745437634829586, 4.085008761969589, 3.878989320060586, 3.838905932328557, 3.784781913848546, 3.7171019434843293, 3.657228121818104, 3.6447949095354675, 3.5998055946822936, 3.3981162330640005, 3.3510977442781886, 3.134214443619918, 2.9998410125443464, 2.9842715247200036]}, \"tinfo\": {\"Term\": [\"trump\", \"clinton\", \"hillary\", \"donald\", \"photos\", \"facebook\", \"court\", \"check\", \"sanders\", \"twitter\", \"fashion\", \"pinterest\", \"recipes\", \"gop\", \"bernie\", \"weight\", \"food\", \"style\", \"huffpost\", \"president\", \"supreme\", \"wedding\", \"instagram\", \"kardashian\", \"obama\", \"tumblr\", \"kim\", \"jimmy\", \"study\", \"dress\", \"newsbrief\", \"nicki\", \"minaj\", \"meditation\", \"mindful\", \"tatum\", \"maintaining\", \"channing\", \"nurture\", \"triggers\", \"acupuncture\", \"mindfulness\", \"caregiver\", \"meditate\", \"esteem\", \"consciously\", \"gratitude\", \"remedies\", \"intuition\", \"caregivers\", \"anorexia\", \"angelou\", \"amal\", \"jagger\", \"emotion\", \"striving\", \"redefining\", \"perceive\", \"lifei\", \"feedback\", \"happiness\", \"spiritual\", \"compassion\", \"kindness\", \"qualities\", \"happier\", \"emotions\", \"mindset\", \"habits\", \"relationships\", \"wrap\", \"healing\", \"yoga\", \"feelings\", \"parent\", \"practice\", \"grief\", \"pain\", \"anxiety\", \"life\", \"situations\", \"self\", \"feel\", \"parenting\", \"learning\", \"relationship\", \"teach\", \"feeling\", \"children\", \"child\", \"things\", \"stress\", \"learn\", \"need\", \"kids\", \"know\", \"parents\", \"help\", \"work\", \"ways\", \"sleep\", \"love\", \"don\", \"time\", \"people\", \"make\", \"want\", \"way\", \"good\", \"important\", \"day\", \"like\", \"think\", \"ve\", \"just\", \"health\", \"better\", \"really\", \"world\", \"new\", \"rubio\", \"mitt\", \"shutdown\", \"kasich\", \"marco\", \"mcconnell\", \"pruitt\", \"repeal\", \"mitch\", \"midterm\", \"romney\", \"obamacare\", \"representatives\", \"medicaid\", \"fiorina\", \"aca\", \"kobe\", \"merkel\", \"dems\", \"boehner\", \"pac\", \"treasury\", \"pelosi\", \"congressional\", \"kremlin\", \"exxon\", \"ballot\", \"referendum\", \"rand\", \"medicare\", \"republican\", \"gop\", \"senate\", \"warren\", \"tax\", \"democrats\", \"republicans\", \"sen\", \"mccain\", \"ted\", \"donors\", \"polling\", \"cruz\", \"voters\", \"biden\", \"senator\", \"voter\", \"congress\", \"votes\", \"putin\", \"presidential\", \"vote\", \"candidates\", \"candidate\", \"election\", \"democratic\", \"trump\", \"climate\", \"donald\", \"president\", \"russia\", \"government\", \"campaign\", \"obama\", \"house\", \"state\", \"administration\", \"political\", \"party\", \"care\", \"says\", \"said\", \"health\", \"new\", \"white\", 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              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el5738348092148808966722932\", ldavis_el5738348092148808966722932_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el5738348092148808966722932\", ldavis_el5738348092148808966722932_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el5738348092148808966722932\", ldavis_el5738348092148808966722932_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJhopIELxc3Z",
        "colab_type": "text"
      },
      "source": [
        "<div class=\"alert alert-warning\">\n",
        "    The above chart visualizes the distribution of LDA topic and prints out a list of most used topics\n",
        "    </div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "2CApPzxUgP2X",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}