Part 8.2: TextHero_NLP.ipynb

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    "colab": {
      "name": "Part 8.1: TextHero_NLP.ipynb",
      "provenance": [],
      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_Basics/blob/main/Part%208.1%3A%20TextHero_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "perfect-indie"
      },
      "source": [
        "## TextHero"
      ],
      "id": "perfect-indie"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eastern-texture"
      },
      "source": [
        "Under the hoods, Texthero makes use of multiple NLP and machine learning toolkits such as Gensim, NLTK, SpaCy and scikit-learn. You don't need to install them all separately, pip will take care of that."
      ],
      "id": "eastern-texture"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inside-anniversary"
      },
      "source": [
        "Texthero include tools for:\n",
        "\n",
        "- Preprocess text data: it offers both out-of-the-box solutions but it's also flexible for custom-solutions.\n",
        "- Natural Language Processing: keyphrases and keywords extraction, and named entity recognition.\n",
        "- Text representation: TF-IDF, term frequency, and custom word-embeddings (wip)\n",
        "- Vector space analysis: clustering (K-means, Meanshift, DBSAN and Hierarchical), topic modelling (wip) and interpretation.\n",
        "- Text visualization: vector space visualization, place localization on maps (wip)."
      ],
      "id": "inside-anniversary"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sought-trail"
      },
      "source": [
        "Supported representation algorithms:\n",
        "\n",
        "- Term frequency (count)\n",
        "- Term frequency-inverse document frequency (tfidf)\n",
        "*********************************\n",
        "Supported clustering algorithms:\n",
        "*********************************\n",
        "- K-means (kmeans)\n",
        "- Density-Based Spatial Clustering of Applications with Noise (dbscan)\n",
        "- Meanshift (meanshift)\n",
        "********************************\n",
        "Supported dimensionality reduction algorithms:\n",
        "*******************************\n",
        "\n",
        "- Principal component analysis (pca)\n",
        "- t-distributed stochastic neighbor embedding (tsne)\n",
        "- Non-negative matrix factorization (nmf)"
      ],
      "id": "sought-trail"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "helpful-berry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b71de6a3-869b-4c20-e331-d6ecf910dad1"
      },
      "source": [
        "!pip install texthero"
      ],
      "id": "helpful-berry",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "subjective-moldova",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a4188d9-2908-4b64-8bdc-9b7cd7265486"
      },
      "source": [
        "import texthero\n",
        "help(texthero)"
      ],
      "id": "subjective-moldova",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on package texthero:\n",
            "\n",
            "NAME\n",
            "    texthero - Texthero: python toolkit for text preprocessing, representation and visualization.\n",
            "\n",
            "PACKAGE CONTENTS\n",
            "    extend_pandas\n",
            "    nlp\n",
            "    preprocessing\n",
            "    representation\n",
            "    stop_words\n",
            "    stopwords\n",
            "    visualization\n",
            "\n",
            "DATA\n",
            "    Callable = typing.Callable\n",
            "    List = typing.List\n",
            "    Optional = typing.Optional\n",
            "    Set = typing.Set\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/texthero/__init__.py\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "printable-change"
      },
      "source": [
        "#### Text Preprocessing"
      ],
      "id": "printable-change"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "harmful-windows",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb8509a6-0d24-449a-8029-d23478fc0099"
      },
      "source": [
        "import pandas as pd\n",
        "text=\"It's a pleasant   day at Bangaloré; at / (10:30) am\"\n",
        "series=pd.Series(text)\n",
        "series"
      ],
      "id": "harmful-windows",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It's a pleasant   day at Bangaloré; at / (10:3...\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frozen-constitution"
      },
      "source": [
        "import texthero as hero"
      ],
      "id": "frozen-constitution",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "valued-extra",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c891a1ee-e9fa-417b-8f53-03dcb05e4455"
      },
      "source": [
        "#### Remove digits\n",
        "hero.remove_digits(series)"
      ],
      "id": "valued-extra",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It's a pleasant   day at Bangaloré; at / ( : ) am\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "subject-letters",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58a60027-4832-4c30-f541-d58a4de3b234"
      },
      "source": [
        "#### Remove punctuations\n",
        "hero.remove_punctuation(series)"
      ],
      "id": "subject-letters",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It s a pleasant   day at Bangaloré  at    10 3...\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "south-reverse",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32cd7c86-46b3-49cf-b750-f4e589629f5f"
      },
      "source": [
        "#### Remove Brackets\n",
        "hero.remove_brackets(series)"
      ],
      "id": "south-reverse",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It's a pleasant   day at Bangaloré; at /  am\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "comfortable-operation",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "315eb25a-bff5-419a-f757-00644d920dac"
      },
      "source": [
        "#### Remove diacritics\n",
        "hero.remove_diacritics(series)"
      ],
      "id": "comfortable-operation",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It's a pleasant   day at Bangalore; at / (10:3...\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pleasant-antique",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "169d68ce-a89a-4d32-cf96-0998e3e4e089"
      },
      "source": [
        "#### Remove whitespace\n",
        "hero.remove_whitespace(series)"
      ],
      "id": "pleasant-antique",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It's a pleasant day at Bangaloré; at / (10:30) am\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "baking-aurora",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e90fe474-29c4-4c9e-846a-89521e641b40"
      },
      "source": [
        "#### Remove Stopwords\n",
        "hero.remove_stopwords(series)"
      ],
      "id": "baking-aurora",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    It'  pleasant   day  Bangaloré;  / (10:30) \n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "convenient-proportion",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "caa0bf3e-5d37-44a2-a872-b09a75a3be09"
      },
      "source": [
        "hero.clean(series)"
      ],
      "id": "convenient-proportion",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    pleasant day bangalore\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iraqi-variable",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "8f1c5516-186e-42bf-c754-3e026e2928b0"
      },
      "source": [
        "df = pd.read_csv(\n",
        "   \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
        ")\n",
        "df.sample(10)"
      ],
      "id": "iraqi-variable",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>topic</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>607</th>\n",
              "      <td>Marshall set for Leeds move\\n\\nAll Blacks scru...</td>\n",
              "      <td>rugby</td>\n",
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              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>Keegan hails comeback king Fowler\\n\\nMancheste...</td>\n",
              "      <td>football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>FA decides not to punish Mourinho\\n\\nThe Footb...</td>\n",
              "      <td>football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>West Indies opener Rae mourned\\n\\nFormer West ...</td>\n",
              "      <td>cricket</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>Sri Lankans cleared of misconduct\\n\\nTwo Sri L...</td>\n",
              "      <td>cricket</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>655</th>\n",
              "      <td>Venus stunned by Farina Elia\\n\\nVenus Williams...</td>\n",
              "      <td>tennis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>McClaren eyes Uefa Cup top spot\\n\\nSteve McCla...</td>\n",
              "      <td>football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Wilson back in Kiwi cricket squad\\n\\nFormer Al...</td>\n",
              "      <td>cricket</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>Chelsea 3-0 Portsmouth\\n\\nDidier Drogba scored...</td>\n",
              "      <td>football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>Eriksson warned on Cole comments\\n\\nPremier Le...</td>\n",
              "      <td>football</td>\n",
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            "text/plain": [
              "                                                  text     topic\n",
              "607  Marshall set for Leeds move\\n\\nAll Blacks scru...     rugby\n",
              "306  Keegan hails comeback king Fowler\\n\\nMancheste...  football\n",
              "299  FA decides not to punish Mourinho\\n\\nThe Footb...  football\n",
              "135  West Indies opener Rae mourned\\n\\nFormer West ...   cricket\n",
              "194  Sri Lankans cleared of misconduct\\n\\nTwo Sri L...   cricket\n",
              "655  Venus stunned by Farina Elia\\n\\nVenus Williams...    tennis\n",
              "391  McClaren eyes Uefa Cup top spot\\n\\nSteve McCla...  football\n",
              "149  Wilson back in Kiwi cricket squad\\n\\nFormer Al...   cricket\n",
              "477  Chelsea 3-0 Portsmouth\\n\\nDidier Drogba scored...  football\n",
              "464  Eriksson warned on Cole comments\\n\\nPremier Le...  football"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "excellent-longitude",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "57a2333b-c0fc-41bf-aca3-89eff33806e4"
      },
      "source": [
        "###PCA\n",
        "\n",
        "df['pca'] = (\n",
        "   df['text']\n",
        "   .pipe(hero.clean)\n",
        "   .pipe(hero.tfidf)###vectorizing\n",
        "   .pipe(hero.pca)\n",
        ")\n",
        "hero.scatterplot(df, 'pca', color='topic', title=\"PCA BBC Sport news\")"
      ],
      "id": "excellent-longitude",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w88gMjNLQHBJ"
      },
      "source": [
        "Also, we can \"visualize\" the most common words for each topic with top_words"
      ],
      "id": "w88gMjNLQHBJ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qdb2v75yQFJf",
        "outputId": "006d1e2e-9a22-4c8a-a680-358f8dd5cbb4"
      },
      "source": [
        "NUM_TOP_WORDS = 5\n",
        "df.groupby('topic')['text'].apply(lambda x: hero.top_words(x)[:NUM_TOP_WORDS])"
      ],
      "id": "Qdb2v75yQFJf",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "topic         \n",
              "athletics  the    1731\n",
              "           in      885\n",
              "           to      882\n",
              "           and     665\n",
              "           a       613\n",
              "cricket    the    2246\n",
              "           to     1309\n",
              "           a      1045\n",
              "           in     1007\n",
              "           and     988\n",
              "football   the    4516\n",
              "           to     2641\n",
              "           a      2061\n",
              "           and    1904\n",
              "           in     1580\n",
              "rugby      the    2770\n",
              "           to     1393\n",
              "           a      1210\n",
              "           and    1129\n",
              "           in     1093\n",
              "tennis     the    1527\n",
              "           to      826\n",
              "           in      706\n",
              "           a       587\n",
              "           and     573\n",
              "Name: text, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "august-preservation",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "bdb46a0c-de68-4351-b66d-8c3f5e83bae3"
      },
      "source": [
        "df.head()"
      ],
      "id": "august-preservation",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>topic</th>\n",
              "      <th>pca</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Claxton hunting first major medal\\n\\nBritish h...</td>\n",
              "      <td>athletics</td>\n",
              "      <td>[-0.09104989057576593, 0.10347155373429032]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>O'Sullivan could run in Worlds\\n\\nSonia O'Sull...</td>\n",
              "      <td>athletics</td>\n",
              "      <td>[-0.00045037197177923686, 0.02481619299726644]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Greene sets sights on world title\\n\\nMaurice G...</td>\n",
              "      <td>athletics</td>\n",
              "      <td>[-0.11761165386741548, 0.12877483603725004]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>IAAF launches fight against drugs\\n\\nThe IAAF ...</td>\n",
              "      <td>athletics</td>\n",
              "      <td>[-0.09137916008742447, 0.15409369238921933]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dibaba breaks 5,000m world record\\n\\nEthiopia'...</td>\n",
              "      <td>athletics</td>\n",
              "      <td>[-0.09130212553792584, 0.1350658747907763]</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text  ...                                             pca\n",
              "0  Claxton hunting first major medal\\n\\nBritish h...  ...     [-0.09104989057576593, 0.10347155373429032]\n",
              "1  O'Sullivan could run in Worlds\\n\\nSonia O'Sull...  ...  [-0.00045037197177923686, 0.02481619299726644]\n",
              "2  Greene sets sights on world title\\n\\nMaurice G...  ...     [-0.11761165386741548, 0.12877483603725004]\n",
              "3  IAAF launches fight against drugs\\n\\nThe IAAF ...  ...     [-0.09137916008742447, 0.15409369238921933]\n",
              "4  Dibaba breaks 5,000m world record\\n\\nEthiopia'...  ...      [-0.09130212553792584, 0.1350658747907763]\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "talented-console",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "0bd125bc-7ab8-4479-b9d1-df8d2a4bd00b"
      },
      "source": [
        "import texthero as hero\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\n",
        "    \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
        ")\n",
        "\n",
        "df['tfidf'] = (\n",
        "    df['text']\n",
        "    .pipe(hero.clean)\n",
        "    .pipe(hero.tfidf)\n",
        ")\n",
        "### Kmeans\n",
        "\n",
        "df['kmeans_labels'] = (\n",
        "    df['tfidf']\n",
        "    .pipe(hero.kmeans, n_clusters=5)\n",
        "    .astype(str)\n",
        ")\n",
        "\n",
        "df['pca'] = df['tfidf'].pipe(hero.pca)\n",
        "\n",
        "hero.scatterplot(df, 'pca', color='kmeans_labels', title=\"K-means BBC Sport news\")"
      ],
      "id": "talented-console",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
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