sentiment_analyze_RNN.ipynb

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    "colab": {
      "name": "analyze_RNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/snoop2head/yonsei-exchange-program/blob/master/analyze_RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alcCwHo4q2ku",
        "colab_type": "text"
      },
      "source": [
        "## Using Keras RNN to conduct sentiment analysis on naver movie review dataset(NSMC)\n",
        "* Source: [딥 러닝을 이용한 자연어 처리 입문](https://wikidocs.net/44249)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqrSxyXBrEFn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "d34ea10f-423a-4971-e7ee-a6083e9018aa"
      },
      "source": [
        "!git config --global user.email \"young_ahn@yonsei.ac.kr\"\n",
        "!git config --global user.name \"noopy\"\n",
        "!git clone https://github.com/snoop2head/yonsei-exchange-program/"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'yonsei-exchange-life'...\n",
            "remote: Enumerating objects: 1900, done.\u001b[K\n",
            "remote: Counting objects: 100% (1900/1900), done.\u001b[K\n",
            "remote: Compressing objects: 100% (1854/1854), done.\u001b[K\n",
            "remote: Total 3243 (delta 85), reused 1847 (delta 44), pack-reused 1343\u001b[K\n",
            "Receiving objects: 100% (3243/3243), 117.77 MiB | 4.02 MiB/s, done.\n",
            "Resolving deltas: 100% (207/207), done.\n",
            "Checking out files: 100% (2386/2386), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kyPBD9CsW3u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "8d3cd625-1ac0-4f35-d7c4-8dfd0f050f67"
      },
      "source": [
        "!git clone https://github.com/SOMJANG/Mecab-ko-for-Google-Colab.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Mecab-ko-for-Google-Colab'...\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACnOo19rvF6z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "760dde6e-9721-4d08-cecd-532fc4ac538b"
      },
      "source": [
        "!ls -a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".  ..  .config\tMecab-ko-for-Google-Colab  sample_data\tyonsei-exchange-life\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLUGUinvscnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d32fc332-4ec3-4925-fa28-bf8eb375747b"
      },
      "source": [
        "%cd Mecab-ko-for-Google-Colab"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Mecab-ko-for-Google-Colab\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZRIQycLsjNB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " !bash install_mecab-ko_on_colab190912.sh"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AERkNX20rWP7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "41feed1f-2d99-417c-fb6d-30a5ea7853a1"
      },
      "source": [
        "%cd .."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hofmMzBHuMbl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "8a08590e-3b60-49cf-89bf-8b86f69000f6"
      },
      "source": [
        "%cd yonsei-exchange-program/\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/yonsei-exchange-life\n",
            "analyze_BERT_nsmc.ipynb    data\t\t\t\t    img\n",
            "analyze_RNN.ipynb\t   data_sentiment\t\t    LICENSE\n",
            "_archived\t\t   extract_keyword_tfidf.ipynb\t    README.md\n",
            "best_model.h5\t\t   generate_wordcloud.ipynb\t    README.pdf\n",
            "cluster_departments.ipynb  generate_wordcloud_konlpy.ipynb  requirements.txt\n",
            "collect_reviews.ipynb\t   generate_wordcloud_tfidf.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZCenmuKrZda",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B67PxXJqq2kv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import urllib.request\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "from konlpy.tag import Okt\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfC3oZYrq2ky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "423d5c79-4358-4ba1-b629-ba51b25198d9"
      },
      "source": [
        "urllib.request.urlretrieve(\"https://raw.githubusercontent.com/e9t/nsmc/master/ratings_train.txt\", filename=\"ratings_train.txt\")\n",
        "urllib.request.urlretrieve(\"https://raw.githubusercontent.com/e9t/nsmc/master/ratings_test.txt\", filename=\"ratings_test.txt\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('ratings_test.txt', <http.client.HTTPMessage at 0x7f295034f828>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swFl1MYSq2k2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data = pd.read_table('ratings_train.txt')\n",
        "test_data = pd.read_table('ratings_test.txt')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "Atj2DdqVq2k4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f2adaa3f-b9c1-4781-9269-bf20d1bfa9cc"
      },
      "source": [
        "print('훈련용 리뷰 개수 :',len(train_data)) # 훈련용 리뷰 개수 출력"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "훈련용 리뷰 개수 : 150000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3CL9bs-q2k6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "6e8c7ab8-e766-4d12-a033-ed462c09c3a0"
      },
      "source": [
        "train_data[:5] # 상위 5개 출력"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>0</th>\n",
              "      <td>9976970</td>\n",
              "      <td>아 더빙.. 진짜 짜증나네요 목소리</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3819312</td>\n",
              "      <td>흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10265843</td>\n",
              "      <td>너무재밓었다그래서보는것을추천한다</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9045019</td>\n",
              "      <td>교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6483659</td>\n",
              "      <td>사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id                                           document  label\n",
              "0   9976970                                아 더빙.. 진짜 짜증나네요 목소리      0\n",
              "1   3819312                  흠...포스터보고 초딩영화줄....오버연기조차 가볍지 않구나      1\n",
              "2  10265843                                  너무재밓었다그래서보는것을추천한다      0\n",
              "3   9045019                      교도소 이야기구먼 ..솔직히 재미는 없다..평점 조정      0\n",
              "4   6483659  사이몬페그의 익살스런 연기가 돋보였던 영화!스파이더맨에서 늙어보이기만 했던 커스틴 ...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "Zmwi2Y_aq2k8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3c32b4ee-cbf0-46a3-fe57-3f4aebd3b05e"
      },
      "source": [
        "print('테스트용 리뷰 개수 :',len(test_data)) # 테스트용 리뷰 개수 출력"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "테스트용 리뷰 개수 : 50000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0viVwqTq2k-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "fc23f1b2-3490-41cb-dff4-156200e5ad57"
      },
      "source": [
        "test_data[:5]"
      ],
      "execution_count": null,
      "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>id</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6270596</td>\n",
              "      <td>굳 ㅋ</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9274899</td>\n",
              "      <td>GDNTOPCLASSINTHECLUB</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8544678</td>\n",
              "      <td>뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6825595</td>\n",
              "      <td>지루하지는 않은데 완전 막장임... 돈주고 보기에는....</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6723715</td>\n",
              "      <td>3D만 아니었어도 별 다섯 개 줬을텐데.. 왜 3D로 나와서 제 심기를 불편하게 하죠??</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                           document  label\n",
              "0  6270596                                                굳 ㅋ      1\n",
              "1  9274899                               GDNTOPCLASSINTHECLUB      0\n",
              "2  8544678             뭐야 이 평점들은.... 나쁘진 않지만 10점 짜리는 더더욱 아니잖아      0\n",
              "3  6825595                   지루하지는 않은데 완전 막장임... 돈주고 보기에는....      0\n",
              "4  6723715  3D만 아니었어도 별 다섯 개 줬을텐데.. 왜 3D로 나와서 제 심기를 불편하게 하죠??      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSCYFzUQq2lB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "412cc82a-deef-4386-b5cd-b2d23f8288ad"
      },
      "source": [
        "train_data['document'].nunique(), train_data['label'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(146182, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTH9rRqFq2lD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data.drop_duplicates(subset=['document'], inplace=True) # document 열에서 중복인 내용이 있다면 중복 제거"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "4eyfIl4iq2lF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b602fe20-a3ec-4d1a-fbff-ffef42c77447"
      },
      "source": [
        "print('총 샘플의 수 :',len(train_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "총 샘플의 수 : 146183\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qV3VlLtq2lH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_data['label'].value_counts().plot(kind = 'bar')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "dKlWM7Z2q2lJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "0356eaf2-0379-4021-9724-041eb34c6cd0"
      },
      "source": [
        "print(train_data.groupby('label').size().reset_index(name = 'count'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   label  count\n",
            "0      0  73342\n",
            "1      1  72841\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "vInxbvCQq2lL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5f0efb55-da88-474c-f3a8-aa18d9d16ece"
      },
      "source": [
        "print(train_data.isnull().values.any())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "9mf7HnR7q2lN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "bcf4efa5-525f-4e49-ff2c-befb349ce004"
      },
      "source": [
        "print(train_data.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id          0\n",
            "document    1\n",
            "label       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Hjv3-Mqq2lP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "a05f06cb-6032-4ecd-d9a1-58c775a093ef"
      },
      "source": [
        "train_data.loc[train_data.document.isnull()]"
      ],
      "execution_count": null,
      "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>id</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25857</th>\n",
              "      <td>2172111</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            id document  label\n",
              "25857  2172111      NaN      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "U__XnDRRq2lS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "344b0d71-0105-4ef5-81d6-d2712e9c771a"
      },
      "source": [
        "train_data = train_data.dropna(how = 'any') # Null 값이 존재하는 행 제거\n",
        "print(train_data.isnull().values.any()) # Null 값이 존재하는지 확인"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "FgV5xb2gq2lU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "960ad874-f4bb-4c86-cfe3-f3710f44f4fd"
      },
      "source": [
        "print(len(train_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "146182\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tB7Xokvoq2lW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "3fe492cd-7707-4738-9f9d-e5067d8ef317"
      },
      "source": [
        "train_data['document'] = train_data['document'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\")\n",
        "# 한글과 공백을 제외하고 모두 제거\n",
        "train_data[:5]"
      ],
      "execution_count": null,
      "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>id</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9976970</td>\n",
              "      <td>아 더빙 진짜 짜증나네요 목소리</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3819312</td>\n",
              "      <td>흠포스터보고 초딩영화줄오버연기조차 가볍지 않구나</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10265843</td>\n",
              "      <td>너무재밓었다그래서보는것을추천한다</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9045019</td>\n",
              "      <td>교도소 이야기구먼 솔직히 재미는 없다평점 조정</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6483659</td>\n",
              "      <td>사이몬페그의 익살스런 연기가 돋보였던 영화스파이더맨에서 늙어보이기만 했던 커스틴 던...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id                                           document  label\n",
              "0   9976970                                  아 더빙 진짜 짜증나네요 목소리      0\n",
              "1   3819312                         흠포스터보고 초딩영화줄오버연기조차 가볍지 않구나      1\n",
              "2  10265843                                  너무재밓었다그래서보는것을추천한다      0\n",
              "3   9045019                          교도소 이야기구먼 솔직히 재미는 없다평점 조정      0\n",
              "4   6483659  사이몬페그의 익살스런 연기가 돋보였던 영화스파이더맨에서 늙어보이기만 했던 커스틴 던...      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "u3VXgI3Uq2lZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "d5687b52-ea11-4136-8b7b-c967491938df"
      },
      "source": [
        "train_data['document'].replace('', np.nan, inplace=True)\n",
        "print(train_data.isnull().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "id            0\n",
            "document    391\n",
            "label         0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ofvCrYbq2lb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "36d43e09-837d-4ac8-950a-57bd4e96682e"
      },
      "source": [
        "train_data.loc[train_data.document.isnull()][:5]"
      ],
      "execution_count": null,
      "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>id</th>\n",
              "      <th>document</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>7117896</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>593</th>\n",
              "      <td>6478189</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>638</th>\n",
              "      <td>9364602</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>668</th>\n",
              "      <td>1600635</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1559</th>\n",
              "      <td>6918082</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           id document  label\n",
              "584   7117896      NaN      0\n",
              "593   6478189      NaN      0\n",
              "638   9364602      NaN      0\n",
              "668   1600635      NaN      0\n",
              "1559  6918082      NaN      1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "k1RjTfifq2ld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "eb6d56df-bb7e-43a3-c368-4a51776e6c55"
      },
      "source": [
        "train_data = train_data.dropna(how = 'any')\n",
        "print(len(train_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "145791\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "mZQuGWOKq2lf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3bf25c8c-908c-4c21-9a31-32c5257a2d49"
      },
      "source": [
        "test_data.drop_duplicates(subset = ['document'], inplace=True) # document 열에서 중복인 내용이 있다면 중복 제거\n",
        "test_data['document'] = test_data['document'].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\") # 정규 표현식 수행\n",
        "test_data['document'].replace('', np.nan, inplace=True) # 공백은 Null 값으로 변경\n",
        "test_data = test_data.dropna(how='any') # Null 값 제거\n",
        "print('전처리 후 테스트용 샘플의 개수 :',len(test_data))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "전처리 후 테스트용 샘플의 개수 : 48995\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWH0bsywq2lh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stopwords = ['의','가','이','은','들','는','좀','잘','걍','과','도','를','으로','자','에','와','한','하다']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMHHbYB5q2li",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a6ba6b04-4c1a-4f8d-df11-1c39a9376f88"
      },
      "source": [
        "okt = Okt()\n",
        "okt.morphs('와 이런 것도 영화라고 차라리 뮤직비디오를 만드는 게 나을 뻔', stem = True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['오다', '이렇다', '것', '도', '영화', '라고', '차라리', '뮤직비디오', '를', '만들다', '게', '나다', '뻔']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6AXTKMbq2lk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = []\n",
        "for sentence in train_data['document']:\n",
        "    temp_X = []\n",
        "    temp_X = okt.morphs(sentence, stem=True) # 토큰화\n",
        "    temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거\n",
        "    X_train.append(temp_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "8kh4kj_Tq2lm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b3ce1b19-8cb4-4548-8891-53af8ad18873"
      },
      "source": [
        "print(X_train[:3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['아', '더빙', '진짜', '짜증나다', '목소리'], ['흠', '포스터', '보고', '초딩', '영화', '줄', '오버', '연기', '조차', '가볍다', '않다'], ['너', '무재', '밓었', '다그', '래서', '보다', '추천', '다']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZD57nPkq2lo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = []\n",
        "for sentence in test_data['document']:\n",
        "    temp_X = []\n",
        "    temp_X = okt.morphs(sentence, stem=True) # 토큰화\n",
        "    temp_X = [word for word in temp_X if not word in stopwords] # 불용어 제거\n",
        "    X_test.append(temp_X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqdaIbbUq2lq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "outputPrepend"
        ],
        "id": "egzVH49iq2lt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(tokenizer.word_index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "UOSZRBnAq2lu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "90cd5419-5caa-4941-af2c-3fb6041aefb4"
      },
      "source": [
        "threshold = 3\n",
        "total_cnt = len(tokenizer.word_index) # 단어의 수\n",
        "rare_cnt = 0 # 등장 빈도수가 threshold보다 작은 단어의 개수를 카운트\n",
        "total_freq = 0 # 훈련 데이터의 전체 단어 빈도수 총 합\n",
        "rare_freq = 0 # 등장 빈도수가 threshold보다 작은 단어의 등장 빈도수의 총 합\n",
        "\n",
        "# 단어와 빈도수의 쌍(pair)을 key와 value로 받는다.\n",
        "for key, value in tokenizer.word_counts.items():\n",
        "    total_freq = total_freq + value\n",
        "\n",
        "    # 단어의 등장 빈도수가 threshold보다 작으면\n",
        "    if(value < threshold):\n",
        "        rare_cnt = rare_cnt + 1\n",
        "        rare_freq = rare_freq + value\n",
        "\n",
        "print('단어 집합(vocabulary)의 크기 :',total_cnt)\n",
        "print('등장 빈도가 %s번 이하인 희귀 단어의 수: %s'%(threshold - 1, rare_cnt))\n",
        "print(\"단어 집합에서 희귀 단어의 비율:\", (rare_cnt / total_cnt)*100)\n",
        "print(\"전체 등장 빈도에서 희귀 단어 등장 빈도 비율:\", (rare_freq / total_freq)*100)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "단어 집합(vocabulary)의 크기 : 43752\n",
            "등장 빈도가 2번 이하인 희귀 단어의 수: 24337\n",
            "단어 집합에서 희귀 단어의 비율: 55.62488571950996\n",
            "전체 등장 빈도에서 희귀 단어 등장 빈도 비율: 1.8715872104872904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "XQBJEs_Lq2lw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8a4aed88-b38b-4849-8b1a-915f86ef5d1d"
      },
      "source": [
        "# 전체 단어 개수 중 빈도수 2이하인 단어 개수는 제거.\n",
        "# 0번 패딩 토큰과 1번 OOV 토큰을 고려하여 +2\n",
        "vocab_size = total_cnt - rare_cnt + 2\n",
        "print('단어 집합의 크기 :',vocab_size)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "단어 집합의 크기 : 19417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YSb8kTxq2ly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tokenizer = Tokenizer(vocab_size, oov_token = 'OOV') \n",
        "tokenizer.fit_on_texts(X_train)\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "QVuIk7ZPq2l0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "50e8ea33-2ff5-4662-c8a2-87263419f2e5"
      },
      "source": [
        "print(X_train[:3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[51, 455, 17, 261, 660], [934, 458, 42, 603, 2, 215, 1450, 25, 962, 676, 20], [387, 2445, 1, 2316, 5672, 3, 223, 10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8OYN29Nq2l1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train = np.array(train_data['label'])\n",
        "y_test = np.array(test_data['label'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxBBHLw4q2l3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "drop_train = [index for index, sentence in enumerate(X_train) if len(sentence) < 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "Gvox0gFxq2l5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e1ad8b10-4c46-421d-9e06-8373e2300305"
      },
      "source": [
        "# 빈 샘플들을 제거\n",
        "X_train = np.delete(X_train, drop_train, axis=0)\n",
        "y_train = np.delete(y_train, drop_train, axis=0)\n",
        "print(len(X_train))\n",
        "print(len(y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "145380\n",
            "145380\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "MlDak49vq2l6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print('리뷰의 최대 길이 :',max(len(l) for l in X_train))\n",
        "print('리뷰의 평균 길이 :',sum(map(len, X_train))/len(X_train))\n",
        "plt.hist([len(s) for s in X_train], bins=50)\n",
        "plt.xlabel('length of samples')\n",
        "plt.ylabel('number of samples')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tP05vFOq2l8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def below_threshold_len(max_len, nested_list):\n",
        "  cnt = 0\n",
        "  for s in nested_list:\n",
        "    if(len(s) <= max_len):\n",
        "        cnt = cnt + 1\n",
        "  print('전체 샘플 중 길이가 %s 이하인 샘플의 비율: %s'%(max_len, (cnt / len(nested_list))*100))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "u783mJDfq2l-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c11968b3-9967-406c-ab3f-6c0e3d796293"
      },
      "source": [
        "max_len = 50\n",
        "below_threshold_len(max_len, X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "전체 샘플 중 길이가 50 이하인 샘플의 비율: 99.91126702434998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1Hnxbq7q2l_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = pad_sequences(X_train, maxlen = max_len)\n",
        "X_test = pad_sequences(X_test, maxlen = max_len)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEqPS7Kiq2mD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Embedding, Dense, LSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kgoN_jV5q2mF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 100))\n",
        "model.add(LSTM(128))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1pJbTAYq2mI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=4)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "in5yOzNQq2mK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "outputId": "58a3cff7-6761-43b0-b3cf-83d898fa926f"
      },
      "source": [
        "model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\n",
        "history = model.fit(X_train, y_train, epochs=15, callbacks=[es, mc], batch_size=60, validation_split=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "1936/1939 [============================>.] - ETA: 0s - loss: 0.3919 - acc: 0.8215\n",
            "Epoch 00001: val_acc improved from -inf to 0.84276, saving model to best_model.h5\n",
            "1939/1939 [==============================] - 26s 14ms/step - loss: 0.3917 - acc: 0.8216 - val_loss: 0.3571 - val_acc: 0.8428\n",
            "Epoch 2/15\n",
            "1936/1939 [============================>.] - ETA: 0s - loss: 0.3278 - acc: 0.8578\n",
            "Epoch 00002: val_acc improved from 0.84276 to 0.85555, saving model to best_model.h5\n",
            "1939/1939 [==============================] - 25s 13ms/step - loss: 0.3278 - acc: 0.8578 - val_loss: 0.3308 - val_acc: 0.8556\n",
            "Epoch 3/15\n",
            "1936/1939 [============================>.] - ETA: 0s - loss: 0.3017 - acc: 0.8716\n",
            "Epoch 00003: val_acc improved from 0.85555 to 0.85937, saving model to best_model.h5\n",
            "1939/1939 [==============================] - 25s 13ms/step - loss: 0.3016 - acc: 0.8716 - val_loss: 0.3277 - val_acc: 0.8594\n",
            "Epoch 4/15\n",
            "1937/1939 [============================>.] - ETA: 0s - loss: 0.2828 - acc: 0.8819\n",
            "Epoch 00004: val_acc did not improve from 0.85937\n",
            "1939/1939 [==============================] - 24s 12ms/step - loss: 0.2827 - acc: 0.8820 - val_loss: 0.3412 - val_acc: 0.8550\n",
            "Epoch 5/15\n",
            "1938/1939 [============================>.] - ETA: 0s - loss: 0.2670 - acc: 0.8903\n",
            "Epoch 00005: val_acc improved from 0.85937 to 0.86037, saving model to best_model.h5\n",
            "1939/1939 [==============================] - 24s 12ms/step - loss: 0.2671 - acc: 0.8903 - val_loss: 0.3231 - val_acc: 0.8604\n",
            "Epoch 6/15\n",
            "1938/1939 [============================>.] - ETA: 0s - loss: 0.2521 - acc: 0.8976\n",
            "Epoch 00006: val_acc did not improve from 0.86037\n",
            "1939/1939 [==============================] - 24s 13ms/step - loss: 0.2521 - acc: 0.8975 - val_loss: 0.3332 - val_acc: 0.8581\n",
            "Epoch 7/15\n",
            "1939/1939 [==============================] - ETA: 0s - loss: 0.2377 - acc: 0.9042\n",
            "Epoch 00007: val_acc did not improve from 0.86037\n",
            "1939/1939 [==============================] - 24s 13ms/step - loss: 0.2377 - acc: 0.9042 - val_loss: 0.3456 - val_acc: 0.8529\n",
            "Epoch 8/15\n",
            "1935/1939 [============================>.] - ETA: 0s - loss: 0.2222 - acc: 0.9115\n",
            "Epoch 00008: val_acc did not improve from 0.86037\n",
            "1939/1939 [==============================] - 25s 13ms/step - loss: 0.2223 - acc: 0.9115 - val_loss: 0.3411 - val_acc: 0.8553\n",
            "Epoch 9/15\n",
            "1937/1939 [============================>.] - ETA: 0s - loss: 0.2067 - acc: 0.9186\n",
            "Epoch 00009: val_acc did not improve from 0.86037\n",
            "1939/1939 [==============================] - 26s 13ms/step - loss: 0.2068 - acc: 0.9186 - val_loss: 0.3631 - val_acc: 0.8511\n",
            "Epoch 00009: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "7gBiOLcmq2mN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "3fe1037b-aea7-4932-b77f-448b60344111"
      },
      "source": [
        "loaded_model = load_model('best_model.h5')\n",
        "print(\"\\n 테스트 정확도: %.4f\" % (loaded_model.evaluate(X_test, y_test)[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1532/1532 [==============================] - 6s 4ms/step - loss: 0.3294 - acc: 0.8548\n",
            "\n",
            " 테스트 정확도: 0.8548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mpkk7TDhq2mP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sentiment_predict(new_sentence):\n",
        "  new_sentence = okt.morphs(new_sentence, stem=True) # 토큰화\n",
        "  new_sentence = [word for word in new_sentence if not word in stopwords] # 불용어 제거\n",
        "  encoded = tokenizer.texts_to_sequences([new_sentence]) # 정수 인코딩\n",
        "  pad_new = pad_sequences(encoded, maxlen = max_len) # 패딩\n",
        "  score = float(loaded_model.predict(pad_new)) # 예측\n",
        "  if(score > 0.5):\n",
        "    print(\"{:.2f}% 확률로 긍정 리뷰입니다.\\n\".format(score * 100))\n",
        "  else:\n",
        "    print(\"{:.2f}% 확률로 부정 리뷰입니다.\\n\".format((1 - score) * 100))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "Ct_vEUaNq2mR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "fd32141c-0d66-4ce5-e029-0fd7eebee46c"
      },
      "source": [
        "sentiment_predict('이 영화 개꿀잼 ㅋㅋㅋ')\n",
        "sentiment_predict('감독 뭐하는 놈이냐?')\n",
        "sentiment_predict('이 영화 핵노잼 ㅠㅠ')\n",
        "sentiment_predict('이딴게 영화냐 ㅉㅉ')\n",
        "sentiment_predict('와 개쩐다 정말 세계관 최강자들의 영화다')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "92.34% 확률로 긍정 리뷰입니다.\n",
            "\n",
            "98.30% 확률로 부정 리뷰입니다.\n",
            "\n",
            "98.74% 확률로 부정 리뷰입니다.\n",
            "\n",
            "99.85% 확률로 부정 리뷰입니다.\n",
            "\n",
            "90.54% 확률로 긍정 리뷰입니다.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oG4QDNqq2mT",
        "colab_type": "text"
      },
      "source": [
        "### Utilizing it to Yonsei Exchange Reviews"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daKM4JAV1UTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import file related library\n",
        "import os\n",
        "from os import fdopen, remove\n",
        "import glob\n",
        "from tempfile import mkstemp\n",
        "import shutil\n",
        "from shutil import move, copymode"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_AhdhcZq2mV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "source = \"./data/univ_text_data\"\n",
        "source_abstract = \"./data/abstract\"\n",
        "source_specific = \"./data/specific\"\n",
        "source_bert = \"data_sentiment/abstract_bert\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LIJ8veoq2mW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "23170e9a-0540-481b-da43-cbed1d623b6d"
      },
      "source": [
        "abstract_yonsei_reviews = glob.glob(f\"{source_abstract}/*.csv\")\n",
        "abstract_yonsei_reviews[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['./data/abstract/US000102_review_abstract.csv',\n",
              " './data/abstract/CZ000001_review_abstract.csv',\n",
              " './data/abstract/US000030_review_abstract.csv',\n",
              " './data/abstract/US000068_review_abstract.csv',\n",
              " './data/abstract/FR000002_review_abstract.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IWZWX_cq2mY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "7ae33f43-9548-422e-d5a3-ca3ccad283cf"
      },
      "source": [
        "sample_file = abstract_yonsei_reviews[100]\n",
        "df_abstract = pd.read_csv(sample_file, encoding=\"utf-8\")\n",
        "df_abstract.sample(5)"
      ],
      "execution_count": null,
      "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>No</th>\n",
              "      <th>제목</th>\n",
              "      <th>학과</th>\n",
              "      <th>과정</th>\n",
              "      <th>년도</th>\n",
              "      <th>href</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>37</td>\n",
              "      <td>SCSU</td>\n",
              "      <td>심리학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2009</td>\n",
              "      <td>/partner/expReport.asp?id=3989&amp;page=5&amp;bgbn=R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>75</td>\n",
              "      <td>세인트 클라우드 주립대학교</td>\n",
              "      <td>경제학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2013</td>\n",
              "      <td>/partner/expReport.asp?id=6900&amp;page=2&amp;bgbn=R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>76</td>\n",
              "      <td>세인트 클라우드에서의 한 학기</td>\n",
              "      <td>영어영문학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2013</td>\n",
              "      <td>/partner/expReport.asp?id=6922&amp;page=2&amp;bgbn=R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>70</td>\n",
              "      <td>세인트클라우드에서의 소중한 시간</td>\n",
              "      <td>문화인류학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2012</td>\n",
              "      <td>/partner/expReport.asp?id=6077&amp;page=2&amp;bgbn=R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>31</td>\n",
              "      <td>08 SCSU 가을학기</td>\n",
              "      <td>법학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2008</td>\n",
              "      <td>/partner/expReport.asp?id=3743&amp;page=6&amp;bgbn=R</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    No                 제목  ...    년도                                          href\n",
              "49  37               SCSU  ...  2009  /partner/expReport.asp?id=3989&page=5&bgbn=R\n",
              "11  75     세인트 클라우드 주립대학교  ...  2013  /partner/expReport.asp?id=6900&page=2&bgbn=R\n",
              "10  76   세인트 클라우드에서의 한 학기  ...  2013  /partner/expReport.asp?id=6922&page=2&bgbn=R\n",
              "16  70  세인트클라우드에서의 소중한 시간  ...  2012  /partner/expReport.asp?id=6077&page=2&bgbn=R\n",
              "55  31       08 SCSU 가을학기  ...  2008  /partner/expReport.asp?id=3743&page=6&bgbn=R\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "id": "_D-h9A0oq2ma",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "e0a8466a-35b4-4014-d1d0-665b89da7215"
      },
      "source": [
        "one_review_title = df_abstract[\"제목\"][16]\n",
        "print(one_review_title)\n",
        "sentiment_predict(one_review_title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "세인트클라우드에서의 소중한 시간\n",
            "76.41% 확률로 긍정 리뷰입니다.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBLTi47hq2md",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def label_review(series_object):\n",
        "    list_scores = []\n",
        "    list_titles = series_object.to_list()\n",
        "    for item in list_titles:\n",
        "        item = okt.morphs(item, stem=True) # 토큰화\n",
        "        item = [word for word in item if not word in stopwords] # 불용어 제거\n",
        "        encoded = tokenizer.texts_to_sequences([item]) # 정수 인코딩\n",
        "        pad_new = pad_sequences(encoded, maxlen = max_len) # 패딩\n",
        "        score = float(loaded_model.predict(pad_new)) # 예측\n",
        "        list_scores.append(score)\n",
        "    return list_scores"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBAHq2U4q2mi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dest_abstract_sentiment = \"./data_sentiment/abstract\"\n",
        "source_bert = \"./data_sentiment/abstract_bert\"\n",
        "dest_specific_sentiment = \"./data_sentiment/specific\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lUniomnlq2ml",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "0da10476-2137-400b-e34b-0ad6551a1b12"
      },
      "source": [
        "abstract_yonsei_reviews_bert_labeled = glob.glob(f\"{source_bert}/*.csv\")\n",
        "abstract_yonsei_reviews_bert_labeled[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['./data_sentiment/abstract_bert/US000182_review_abstract_BERT.csv',\n",
              " './data_sentiment/abstract_bert/US000241_review_abstract_BERT.csv',\n",
              " './data_sentiment/abstract_bert/CA000008_review_abstract_BERT.csv',\n",
              " './data_sentiment/abstract_bert/US000016_review_abstract_BERT.csv',\n",
              " './data_sentiment/abstract_bert/ES000011_review_abstract_BERT.csv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNuz7oUjq2mk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "dee1d4cf-0b25-4eeb-9e24-d40a69819dee"
      },
      "source": [
        "sample_file = abstract_yonsei_reviews_bert_labeled[50]\n",
        "file_name = sample_file.split(\"/\")[-1]\n",
        "file_name_without_ext = file_name.split(\".\")[0]\n",
        "df_abstract = pd.read_csv(sample_file, encoding=\"utf-8\")\n",
        "df_abstract[\"RNN_SCORE\"] = label_review(df_abstract[\"제목\"])\n",
        "df_abstract.sample(5)"
      ],
      "execution_count": null,
      "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>No</th>\n",
              "      <th>제목</th>\n",
              "      <th>학과</th>\n",
              "      <th>과정</th>\n",
              "      <th>년도</th>\n",
              "      <th>href</th>\n",
              "      <th>BERT_SCORE</th>\n",
              "      <th>RNN_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>25</td>\n",
              "      <td>후회하지 않을 선택 리투아니아</td>\n",
              "      <td>경제학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2017</td>\n",
              "      <td>/partner/expReport.asp?id=14024&amp;page=2&amp;bgbn=R</td>\n",
              "      <td>1</td>\n",
              "      <td>0.864502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5</td>\n",
              "      <td>카우나스 과학기술대를 소개합니다.</td>\n",
              "      <td>사회학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2010</td>\n",
              "      <td>/partner/expReport.asp?id=4795&amp;page=4&amp;bgbn=R</td>\n",
              "      <td>1</td>\n",
              "      <td>0.451056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>22</td>\n",
              "      <td>LITHUANIA - Kaunas University of Technology</td>\n",
              "      <td>UIC</td>\n",
              "      <td>학부</td>\n",
              "      <td>2016</td>\n",
              "      <td>/partner/expReport.asp?id=13455&amp;page=2&amp;bgbn=R</td>\n",
              "      <td>1</td>\n",
              "      <td>0.512364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>10</td>\n",
              "      <td>카우나스에서의 생활</td>\n",
              "      <td>전기전자공학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2012</td>\n",
              "      <td>/partner/expReport.asp?id=6016&amp;page=3&amp;bgbn=R</td>\n",
              "      <td>0</td>\n",
              "      <td>0.602492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>26</td>\n",
              "      <td>6개월의 방학</td>\n",
              "      <td>도시공학과</td>\n",
              "      <td>학부</td>\n",
              "      <td>2017</td>\n",
              "      <td>/partner/expReport.asp?id=14075&amp;page=1&amp;bgbn=R</td>\n",
              "      <td>0</td>\n",
              "      <td>0.549282</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    No                                           제목  ... BERT_SCORE RNN_SCORE\n",
              "10  25                             후회하지 않을 선택 리투아니아  ...          1  0.864502\n",
              "30   5                           카우나스 과학기술대를 소개합니다.  ...          1  0.451056\n",
              "13  22  LITHUANIA - Kaunas University of Technology  ...          1  0.512364\n",
              "25  10                                   카우나스에서의 생활  ...          0  0.602492\n",
              "9   26                                      6개월의 방학  ...          0  0.549282\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myEqUatMq2mn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_abstract_df_with_sentiment_score(csv_file):\n",
        "    file_name = csv_file.split(\"/\")[-1]\n",
        "    file_name_without_ext = file_name.split(\".\")[0]\n",
        "    df_abstract = pd.read_csv(csv_file, encoding=\"utf-8\")\n",
        "    df_abstract[\"RNN_SCORE\"] = label_review(df_abstract[\"제목\"])\n",
        "    df_abstract.to_csv(f\"{dest_abstract_sentiment}/{file_name_without_ext}_sentiment.csv\",encoding=\"utf-8\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc09Qlx1q2mo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for csv_file in abstract_yonsei_reviews_bert_labeled:\n",
        "   make_abstract_df_with_sentiment_score(csv_file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nAa_Ubtq2mp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "87cc072f-2e82-4980-e64e-46953c65d7c8"
      },
      "source": [
        "len(os.listdir(f\"{dest_abstract_sentiment}\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "940"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOBGFJPZ4SXn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "import urllib\n",
        "\n",
        "git_user = input('git username: ')\n",
        "github_email = input('github email address: ')\n",
        "github_user = input('github username(Not email, just username): ')\n",
        "github_password = getpass('github Password: ')\n",
        "github_password = urllib.parse.quote(github_password) # your password is converted into url format\n",
        "github_repo_name = input('Repo name: ')\n",
        "\n",
        "config_git = f'git config --global user.name {git_user}'\n",
        "config_github_email = f'git config --global user.email {github_email}'\n",
        "remove_origin = 'git remote rm origin'\n",
        "add_origin = 'git remote add origin https://{0}:{1}@github.com/{0}/{2}.git'.format(github_user, github_password, github_repo_name)\n",
        "push_command = 'git push --set-upstream origin master'\n",
        "\n",
        "\n",
        "os.system(config_git)\n",
        "os.system(config_github_email)\n",
        "os.system(remove_origin) # commence remove origin\n",
        "os.system(add_origin) # commence add origin to git directory\n",
        "os.system(push_command)\n",
        "\n",
        "git_user, github_email, add_origin, github_password = \"\", \"\", \"\", \"\" # removing the password from the variable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thZg6h3L5Y4x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git add ."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJLusLB56pMf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git commit -m \"add RNN labels to BERT labels\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARUtxf-I6te2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "060e5679-dbc2-455c-e21c-c12ac01bad79"
      },
      "source": [
        "!git push"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counting objects: 475, done.\n",
            "Delta compression using up to 4 threads.\n",
            "Compressing objects: 100% (475/475), done.\n",
            "Writing objects: 100% (475/475), 14.36 MiB | 4.12 MiB/s, done.\n",
            "Total 475 (delta 3), reused 0 (delta 0)\n",
            "remote: Resolving deltas: 100% (3/3), completed with 1 local object.\u001b[K\n",
            "To https://github.com/snoop2head/yonsei-exchange-life.git\n",
            "   ac38b00..87bda23  master -> master\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvUfWGAq6vCt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}