rapid_textmining.ipynb

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    "colab": {
      "name": "rapid_textmining.ipynb",
      "provenance": [],
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/plenoi/CMU_DataScience/blob/master/rapid_textmining.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "metadata": {
        "id": "xetsznYt6mlD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install pythainlp\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pythainlp\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# rm_main is a mandatory function, \n",
        "# the number of arguments has to be the number of input ports (can be none)\n",
        "def rm_main():\n",
        "  # Load Data\n",
        "  df_train = pd.read_csv('https://raw.githubusercontent.com/plenoi/CMU_DataScience/master/train.txt',sep='|',header=None)\n",
        "  df_train_tokenized = []\n",
        "  # Tokenization\n",
        "  for i in range(0,len(df_train)):\n",
        "    df_train_tokenized.append(' '.join(pythainlp.tokenize.word_tokenize(df_train.iloc[i][0])))\n",
        "    \n",
        "  cv = TfidfVectorizer(tokenizer=lambda x:x.split(),ngram_range = (1,1), stop_words =[\"ฉัน\",\"ผม\"],\n",
        "                        min_df=10,max_df=0.9,norm=None,\n",
        "                        use_idf=False,smooth_idf=False)\n",
        "  cv.fit(df_train_tokenized)\n",
        "  X = cv.transform(df_train_tokenized).toarray()\n",
        "  y = df_train.iloc[:][1].values\n",
        "  combined = np.concatenate((y.reshape(-1,1),X),axis=1)\n",
        "  df_result = pd.DataFrame(combined)\n",
        "return df_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95XGioHcKcmX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "607f958e-a385-4577-f63f-b5e53f3743b7"
      },
      "source": [
        "df_result.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 5590 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0     1     2     3     4     5     ...  5584  5585  5586  5587  5588  5589\n",
              "0   3.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "1   3.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "2   5.0   0.0   0.0   1.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "3   3.0   0.0   1.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "4   4.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   0.0   0.0   0.0   0.0   0.0\n",
              "\n",
              "[5 rows x 5590 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wgoM4LBKd5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}