NLP_추가자료1.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import urllib.request\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ]
    },
    {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "총 샘플의 수 : 200000\n"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/NLP/shopping.txt', sep = '\\t', header = None)\n",
        "print('총 샘플의 수 :',len(data))"
      ]
    },
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              "    <tr>\n",
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              "      <td>아주좋아요 바지 정말 좋아서2개 더 구매했어요 이가격에 대박입니다. 바느질이 조금 ...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
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            "text/plain": [
              "   0                                                  1\n",
              "0  5                                            배공빠르고 굿\n",
              "1  2                      택배가 엉망이네용 저희집 밑에층에 말도없이 놔두고가고\n",
              "2  5  아주좋아요 바지 정말 좋아서2개 더 구매했어요 이가격에 대박입니다. 바느질이 조금 ...\n",
              "3  2  선물용으로 빨리 받아서 전달했어야 하는 상품이었는데 머그컵만 와서 당황했습니다. 전...\n",
              "4  5                  민트색상 예뻐요. 옆 손잡이는 거는 용도로도 사용되네요 ㅎㅎ"
            ]
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          "metadata": {},
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        "data.head()"
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      "source": [
        "data['label'] = np.select([data[0] > 3], [1], default=0) # 3 안되는 수 0으로."
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              "      <th>3</th>\n",
              "      <td>2</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5576d58d-dc45-4158-90ed-a4d4006337e1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   0                                                  1  label\n",
              "0  5                                            배공빠르고 굿      1\n",
              "1  2                      택배가 엉망이네용 저희집 밑에층에 말도없이 놔두고가고      0\n",
              "2  5  아주좋아요 바지 정말 좋아서2개 더 구매했어요 이가격에 대박입니다. 바느질이 조금 ...      1\n",
              "3  2  선물용으로 빨리 받아서 전달했어야 하는 상품이었는데 머그컵만 와서 당황했습니다. 전...      0\n",
              "4  5                  민트색상 예뻐요. 옆 손잡이는 거는 용도로도 사용되네요 ㅎㅎ      1"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ilgyUXNOPAXQ",
        "outputId": "f42eabcd-cba0-4dd6-e724-2bfc21f3a283"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "199908"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.drop_duplicates(subset=[1], inplace=True)\n",
        "len(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxTCra6oPfgj",
        "outputId": "44cc773e-e883-413a-f1d3-9e3445b8ad5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        0\n",
              "1        0\n",
              "label    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R3-22SBqQBa_"
      },
      "outputs": [],
      "source": [
        "train_data, test_data = train_test_split(data, test_size = 0.2, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mU0FHC2VQIhl",
        "outputId": "78be106a-2da7-4081-e5ab-b716f30e4bf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "159926\n",
            "39982\n"
          ]
        }
      ],
      "source": [
        "print(len(train_data))\n",
        "print(len(test_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qnt2YIqPol4",
        "outputId": "0ae5da2b-62f0-4374-8175-82a0fb9aa635"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-14-7543f700a5e0>:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  train_data[1] = train_data[1].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣\\s]\",\"\") #공백문자 제외.\n"
          ]
        }
      ],
      "source": [
        "train_data[1] = train_data[1].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣\\s]\",\"\") #공백문자 제외.\n",
        "train_data[1].replace('', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "hhXgTCRKQmRA",
        "outputId": "c665ab5f-6ebb-46fb-ebb8-b9c03ffd575d"
      },
      "outputs": [
        {
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              "      <td>0</td>\n",
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              "      <th>188461</th>\n",
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              "      <td>시중에 판매하는 건새우랑 다르게 속살이 고루분포된게아니라 속이비어보이고 속살이 한쪽...</td>\n",
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              "      <th>124757</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2dd42965-7cc4-44ac-b267-82cb8392c305 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2dd42965-7cc4-44ac-b267-82cb8392c305');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "        0                                                  1  label\n",
              "132466  1     바디버든 줄이려고 써보려고 샀는데 음용할수잇는 물로 헹궈야되면 왜 식기세척기 쓰나요      0\n",
              "188461  2  시중에 판매하는 건새우랑 다르게 속살이 고루분포된게아니라 속이비어보이고 속살이 한쪽...      0\n",
              "124757  4                                     깔끔하니 이쁘네요 좋습니다      1\n",
              "50357   2                                사이즈미스 남자가 쓰기엔 너무작네요      0\n",
              "137655  4                             좋은 상품 빠르고 안전한 배송 감사합니다      1"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1PdohlOQoke",
        "outputId": "9481a777-a7fb-4699-c024-cf1c49ff80d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        0\n",
              "1        0\n",
              "label    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIPDyp-qQxQO",
        "outputId": "738a154b-e060-494e-abf0-a2c0ea8f85d5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-16-38e06aaf39e4>:1: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  test_data[1] = data[1].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\") # 정규 표현식 수행\n"
          ]
        }
      ],
      "source": [
        "test_data[1] = data[1].str.replace(\"[^ㄱ-ㅎㅏ-ㅣ가-힣 ]\",\"\") # 정규 표현식 수행\n",
        "test_data[1].replace('', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "C3zZD1bpQ-Xn",
        "outputId": "a5c887eb-6f2a-4701-f8b2-b05410c1870a"
      },
      "outputs": [
        {
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              "      <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",
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              "    .dataframe thead th {\n",
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              "      <th></th>\n",
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              "      <th>1</th>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>125080</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122750</th>\n",
              "      <td>5</td>\n",
              "      <td>배송도 빠르네요 가격대비 좋은것 같아요 첨에는 힘들어하나 조금 지나니 잘 하네요</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
              "      <td>재구매입니다 핏도 좋고 착용감도 좋습니다</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>83890</th>\n",
              "      <td>1</td>\n",
              "      <td>파손제품 온거 출장같다 오늘 받았는데 현재상황 장난하시는지 택배회사 항의하세요</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-899eac07-0360-4d9e-bea7-99fd1d67a389')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-265e7f55-bdc3-403c-a245-97692a9dbcd1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-265e7f55-bdc3-403c-a245-97692a9dbcd1')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-265e7f55-bdc3-403c-a245-97692a9dbcd1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-899eac07-0360-4d9e-bea7-99fd1d67a389 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-899eac07-0360-4d9e-bea7-99fd1d67a389');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        0                                             1  label\n",
              "193242  1                           너무 낮고 솜도 적고 실망스럽습니다      0\n",
              "125080  1                              피부에 뾰루지가 많이 올라와요      0\n",
              "122750  5  배송도 빠르네요 가격대비 좋은것 같아요 첨에는 힘들어하나 조금 지나니 잘 하네요      1\n",
              "72927   5                        재구매입니다 핏도 좋고 착용감도 좋습니다      1\n",
              "83890   1   파손제품 온거 출장같다 오늘 받았는데 현재상황 장난하시는지 택배회사 항의하세요      0"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvuTuJmoRIV9",
        "outputId": "0de202fb-52e5-4b8f-c19c-0b1f6eae6357"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0        0\n",
              "1        0\n",
              "label    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_YvctQvRNDZ",
        "outputId": "d20b1058-ff07-414d-a670-d9ddd72c9be8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting konlpy\n",
            "  Downloading konlpy-0.6.0-py2.py3-none-any.whl (19.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.4/19.4 MB\u001b[0m \u001b[31m41.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting JPype1>=0.7.0 (from konlpy)\n",
            "  Downloading JPype1-1.4.1-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (465 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m465.3/465.3 kB\u001b[0m \u001b[31m42.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from konlpy) (4.9.3)\n",
            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.10/dist-packages (from konlpy) (1.22.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from JPype1>=0.7.0->konlpy) (23.1)\n",
            "Installing collected packages: JPype1, konlpy\n",
            "Successfully installed JPype1-1.4.1 konlpy-0.6.0\n"
          ]
        }
      ],
      "source": [
        "!pip install konlpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SA0ltD6IR2Zi"
      },
      "outputs": [],
      "source": [
        "from konlpy.tag import Okt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D_JLEdNcR9u3"
      },
      "outputs": [],
      "source": [
        "okt = Okt()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6hOGkj_hSPPQ"
      },
      "outputs": [],
      "source": [
        "stopwords = ['도', '는', '다', '의', '가', '이', '은', '한', '에', '하', '고', '을', '를', '인', '듯', '과', '와', '네', '들', '듯', '지', '임', '게']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "Q2trovdeSR8Y"
      },
      "outputs": [],
      "source": [
        "train_data['tokenized'] = train_data[1].apply(okt.morphs)\n",
        "train_data['tokenized'] = train_data[1].apply(lambda x: [item for item in x if item not in stopwords])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "VQu65ly0Shzr"
      },
      "outputs": [],
      "source": [
        "test_data['tokenized'] = test_data[1].apply(okt.morphs) # 형태소 분리.\n",
        "test_data['tokenized'] = test_data[1].apply(lambda x: [item for item in x if item not in stopwords])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "tA7Esm4NTvXj"
      },
      "outputs": [],
      "source": [
        "X_train = train_data['tokenized'].values\n",
        "y_train = train_data['label'].values\n",
        "X_test= test_data['tokenized'].values\n",
        "y_test = test_data['label'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "qIMYw1MNSpR9"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer() ##\n",
        "tokenizer.fit_on_texts(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4UbnYOIOUXg4"
      },
      "outputs": [],
      "source": [
        "total_cnt = len(tokenizer.word_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FsXGFo86UbKo"
      },
      "outputs": [],
      "source": [
        "vocab_size = total_cnt + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I64CpRS_TwHO"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xRYAnzpPUdYU"
      },
      "outputs": [],
      "source": [
        "X_train = pad_sequences(X_train, maxlen = 40)\n",
        "X_test = pad_sequences(X_test, maxlen = 40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lBrhYt9WVV4A"
      },
      "outputs": [],
      "source": [
        "w2i = tokenizer.word_index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M3ut4My-Urf0"
      },
      "outputs": [],
      "source": [
        "vocab_size = len(w2i) + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pVZomXwjWN5Q"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import SimpleRNN, Embedding, Dense, LSTM\n",
        "from tensorflow.keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qr_WjoKPV_VG"
      },
      "outputs": [],
      "source": [
        "model = Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i_784J-LV_ro"
      },
      "outputs": [],
      "source": [
        "model.add(Embedding(vocab_size, 32)) #LSTM 올라가기 전에 각 output값 싹다 32차원 맞춰준다는 말.\n",
        "model.add(SimpleRNN(100, return_sequences = True))\n",
        "model.add(SimpleRNN(50)) #\n",
        "model.add(Dense(1, activation = 'sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PwoOaF_lWPOU"
      },
      "outputs": [],
      "source": [
        "#model.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
        "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnqsTXXgWRvx",
        "outputId": "2c8da1a0-6bde-4e36-95d3-1875f8ce0ff7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "3999/3999 [==============================] - 128s 31ms/step - loss: 0.3911 - accuracy: 0.8337 - val_loss: 0.3569 - val_accuracy: 0.8557\n",
            "Epoch 2/5\n",
            "3999/3999 [==============================] - 122s 31ms/step - loss: 0.3640 - accuracy: 0.8540 - val_loss: 0.3727 - val_accuracy: 0.8496\n",
            "Epoch 3/5\n",
            "3999/3999 [==============================] - 124s 31ms/step - loss: 0.3435 - accuracy: 0.8653 - val_loss: 0.3462 - val_accuracy: 0.8628\n",
            "Epoch 4/5\n",
            "3999/3999 [==============================] - 125s 31ms/step - loss: 0.3387 - accuracy: 0.8669 - val_loss: 0.3385 - val_accuracy: 0.8664\n",
            "Epoch 5/5\n",
            "3999/3999 [==============================] - 121s 30ms/step - loss: 0.3303 - accuracy: 0.8708 - val_loss: 0.3278 - val_accuracy: 0.8683\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(X_train, y_train, epochs = 5, batch_size = 32, validation_split = 0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q9eLTcIDZG7q"
      },
      "outputs": [],
      "source": [
        "xtest_padded = pad_sequences(X_test, maxlen = 48)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLjtxV4sZQdw",
        "outputId": "d56062b8-668d-4327-f423-82f11cc1830a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1250/1250 [==============================] - 13s 10ms/step - loss: 0.3335 - accuracy: 0.8670\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.866965115070343"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "model.evaluate(xtest_padded, y_test)[1]"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}