LSTM_sentiment_analysis_eng.ipynb

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    "colab": {
      "name": "LSTM finance sentiment analysis_eng.ipynb",
      "provenance": [],
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      "machine_shape": "hm"
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import re\n",
        "import urllib.request\n",
        "from tqdm import tqdm\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "Qww_V4GzhgoS"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "urllib.request.urlretrieve(\"https://raw.githubusercontent.com/ukairia777/finance_sentiment_corpus/main/finance_data.csv\", filename=\"finance_data.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8wC6Rnt6iCxB",
        "outputId": "c36e3725-71c3-43e7-8d50-3f205d2ae363"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('finance_data.csv', <http.client.HTTPMessage at 0x7ff00e23bb90>)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('finance_data.csv')\n",
        "print('총 샘플의 수 :',len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aP870z3iRc_",
        "outputId": "73de7c41-c9af-4dac-f046-2b4bf1bb4f65"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "총 샘플의 수 : 4846\n"
          ]
        }
      ]
    },
    {
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        "data"
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      "outputs": [
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              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>negative</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "      <td>국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...</td>\n",
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              "      <td>positive</td>\n",
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              "      <td>새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...</td>\n",
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              "      <td>2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...</td>\n",
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              "      <td>LONDON MarketWatch -- Share prices ended lower...</td>\n",
              "      <td>런던 마켓워치 -- 은행주의 반등이 FTSE 100지수의 약세를 상쇄하지 못하면서 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4842</th>\n",
              "      <td>neutral</td>\n",
              "      <td>Rinkuskiai's beer sales fell by 6.5 per cent t...</td>\n",
              "      <td>린쿠스키아의 맥주 판매량은 416만 리터로 6.5% 감소했으며 카우노 알루스의 맥주...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>Operating profit fell to EUR 35.4 mn from EUR ...</td>\n",
              "      <td>영업이익은 2007년 68.8 mn에서 35.4 mn으로 떨어졌으며, 선박 판매 이...</td>\n",
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              "      <th>4844</th>\n",
              "      <td>negative</td>\n",
              "      <td>Net sales of the Paper segment decreased to EU...</td>\n",
              "      <td>페이퍼 부문 순매출은 2008년 2분기 241.1 mn에서 2009년 2분기 221...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>Sales in Finland decreased by 10.5 % in Januar...</td>\n",
              "      <td>핀란드에서의 판매는 1월에 10.5% 감소한 반면, 국외에서의 판매는 17% 감소했다.</td>\n",
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            "text/plain": [
              "        labels  ...                                       kor_sentence\n",
              "0      neutral  ...  Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...\n",
              "1      neutral  ...  테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...\n",
              "2     negative  ...  국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...\n",
              "3     positive  ...  새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...\n",
              "4     positive  ...  2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...\n",
              "...        ...  ...                                                ...\n",
              "4841  negative  ...  런던 마켓워치 -- 은행주의 반등이 FTSE 100지수의 약세를 상쇄하지 못하면서 ...\n",
              "4842   neutral  ...  린쿠스키아의 맥주 판매량은 416만 리터로 6.5% 감소했으며 카우노 알루스의 맥주...\n",
              "4843  negative  ...  영업이익은 2007년 68.8 mn에서 35.4 mn으로 떨어졌으며, 선박 판매 이...\n",
              "4844  negative  ...  페이퍼 부문 순매출은 2008년 2분기 241.1 mn에서 2009년 2분기 221...\n",
              "4845  negative  ...   핀란드에서의 판매는 1월에 10.5% 감소한 반면, 국외에서의 판매는 17% 감소했다.\n",
              "\n",
              "[4846 rows x 3 columns]"
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        "data['labels'] = data['labels'].replace(['neutral', 'positive', 'negative'],[0, 1, 2])\n",
        "data[:5]"
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              "\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": [
              "   labels  ...                                       kor_sentence\n",
              "0       0  ...  Gran에 따르면, 그 회사는 회사가 성장하고 있는 곳이지만, 모든 생산을 러시아로...\n",
              "1       0  ...  테크노폴리스는 컴퓨터 기술과 통신 분야에서 일하는 회사들을 유치하기 위해 10만 평...\n",
              "2       2  ...  국제 전자산업 회사인 엘코텍은 탈린 공장에서 수십 명의 직원을 해고했으며, 이전의 ...\n",
              "3       1  ...  새로운 생산공장으로 인해 회사는 예상되는 수요 증가를 충족시킬 수 있는 능력을 증가...\n",
              "4       1  ...  2009-2012년 회사의 업데이트된 전략에 따르면, Basware는 20% - 4...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del data['kor_sentence']"
      ],
      "metadata": {
        "id": "RNyrW0ynix3O"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5Pz9t4U2i35Q",
        "outputId": "de701957-621f-4af6-f108-bfe7b0bdc793"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3d030094-7db8-4673-bd7c-436923f95310\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>According to Gran, the company has no plans to...</td>\n",
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              "    <tr>\n",
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              "      <td>Technopolis plans to develop in stages an area...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>The international electronic industry company ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>With the new production plant the company woul...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>According to the company's updated strategy fo...</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3d030094-7db8-4673-bd7c-436923f95310');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   labels                                           sentence\n",
              "0       0  According to Gran, the company has no plans to...\n",
              "1       0  Technopolis plans to develop in stages an area...\n",
              "2       2  The international electronic industry company ...\n",
              "3       1  With the new production plant the company woul...\n",
              "4       1  According to the company's updated strategy fo..."
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7EW631ui4OA",
        "outputId": "a5cb3abd-20aa-4f1a-efda-918d54c577c6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4846 entries, 0 to 4845\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   labels    4846 non-null   int64 \n",
            " 1   sentence  4846 non-null   object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 75.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('결측값 여부 :',data.isnull().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkvjJa1Ji7Eo",
        "outputId": "ca02a8a6-96b0-4973-a038-f7f0920e689b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "결측값 여부 : False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('sentence 열의 유니크한 값 :',data['sentence'].nunique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoAyW4Lii8xX",
        "outputId": "9122137f-bc5d-44ab-e504-e9419d6ded93"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentence 열의 유니크한 값 : 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate = data[data.duplicated()]"
      ],
      "metadata": {
        "id": "cks_QRBfjKBU"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "ZwkZ7T0BjL7d",
        "outputId": "b8bb1b19-1a46-4d7b-c34d-dcdc889729f1"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-311ec74e-b5fc-45a4-b34f-24d9331fb0f6\">\n",
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              "      <th>1099</th>\n",
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              "      <td>The issuer is solely responsible for the conte...</td>\n",
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              "      <td>Ahlstrom's share is quoted on the NASDAQ OMX H...</td>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-311ec74e-b5fc-45a4-b34f-24d9331fb0f6 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-311ec74e-b5fc-45a4-b34f-24d9331fb0f6');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      labels                                           sentence\n",
              "1099       0  The issuer is solely responsible for the conte...\n",
              "1416       0  The report profiles 614 companies including ma...\n",
              "2396       0  Ahlstrom's share is quoted on the NASDAQ OMX H...\n",
              "2567       0  SSH Communications Security Corporation is hea...\n",
              "3094       0  Proha Plc ( Euronext :7327 ) announced today (...\n",
              "3206       0  The company serves customers in various indust..."
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 중복 제거\n",
        "data.drop_duplicates(subset=['sentence'], inplace=True)\n",
        "print('총 샘플의 수 :',len(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prnB1Yg5jBt2",
        "outputId": "8e279f29-7a27-463b-ce56-d5e81bbe5b4d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "총 샘플의 수 : 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['labels'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "StnulPDqjuKX",
        "outputId": "7dd29a19-678b-4f1f-f51d-2c98faa66c49"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff00df3be90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yNwvT1cLx7p0RSfKfwOVV9b/n1C8BjlTV2oXpTBcqyX9X1aqF7kNTS/Iw8K6qej7JauBrwJer6u+S/LCq3r6gDY6Y0zuj8yvgDcBT59SXtW16CUvy0HSbgKWj7EWz9qqzUzpVdTTJlcDXkryRydevK4b+6HwEOJDkCX79o3OrgDcDtyxYVxrWUuAa4Nlz6gH+bfTtaBaeTrK+qh4EaFf8fwrsBP5oYVsbPUN/RKrqO0newuTPSw9+kHuwql5cuM40pG8Brz0bHIOS/Ovo29Es3AicGSxU1RngxiR/vzAtLRzn9CWpI969I0kdMfQlqSOGviR1xNCXpI4Y+pLUkf8DFLnXLhexNawAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('레이블의 분포')\n",
        "print(data.groupby('labels').size().reset_index(name='count'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dP_BFHlojwiN",
        "outputId": "3eb9c43d-af3a-4643-80e0-c4ac4a64bf13"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "레이블의 분포\n",
            "   labels  count\n",
            "0       0   2872\n",
            "1       1   1362\n",
            "2       2    604\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'중립의 비율 = {round(data[\"labels\"].value_counts()[0]/len(data) * 100,3)}%')\n",
        "print(f'긍정의 비율 = {round(data[\"labels\"].value_counts()[1]/len(data) * 100,3)}%')\n",
        "print(f'부정의 비율 = {round(data[\"labels\"].value_counts()[2]/len(data) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CfQdxYqMj3o8",
        "outputId": "e9ec65f4-ce37-49ba-941c-87937b99a830"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "중립의 비율 = 59.363%\n",
            "긍정의 비율 = 28.152%\n",
            "부정의 비율 = 12.484%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "jKKmVrDRk46e",
        "outputId": "4e360ed7-24e9-45cd-ccdc-a5a794c24522"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>Net sales of the Paper segment decreased to EU...</td>\n",
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              "    <tr>\n",
              "      <th>4845</th>\n",
              "      <td>2</td>\n",
              "      <td>Sales in Finland decreased by 10.5 % in Januar...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4838 rows × 2 columns</p>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-277c54da-aa69-4073-8bf2-0b4f96fc524b');\n",
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              "            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",
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            ],
            "text/plain": [
              "      labels                                           sentence\n",
              "0          0  According to Gran, the company has no plans to...\n",
              "1          0  Technopolis plans to develop in stages an area...\n",
              "2          2  The international electronic industry company ...\n",
              "3          1  With the new production plant the company woul...\n",
              "4          1  According to the company's updated strategy fo...\n",
              "...      ...                                                ...\n",
              "4841       2  LONDON MarketWatch -- Share prices ended lower...\n",
              "4842       0  Rinkuskiai's beer sales fell by 6.5 per cent t...\n",
              "4843       2  Operating profit fell to EUR 35.4 mn from EUR ...\n",
              "4844       2  Net sales of the Paper segment decreased to EU...\n",
              "4845       2  Sales in Finland decreased by 10.5 % in Januar...\n",
              "\n",
              "[4838 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_data = data['sentence']\n",
        "y_data = data['labels']\n",
        "print('본문의 개수: {}'.format(len(X_data)))\n",
        "print('레이블의 개수: {}'.format(len(y_data)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8foVC2rkJtF",
        "outputId": "942ef18e-9b62-4b7b-dc70-ef7175c6c906"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "본문의 개수: 4838\n",
            "레이블의 개수: 4838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.2, random_state=0, stratify=y_data)"
      ],
      "metadata": {
        "id": "qmFecWmSkHiu"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('훈련 샘플의 개수 :', len(X_train))\n",
        "print('테스트 샘플의 개수 :', len(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HiPJQoBql2T_",
        "outputId": "f7fdc884-0179-4dbe-8904-d2291dfda22d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "훈련 샘플의 개수 : 3870\n",
            "테스트 샘플의 개수 : 968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('--------훈련 데이터의 비율-----------')\n",
        "print(f'중립 = {round(y_train.value_counts()[0]/len(y_train) * 100,3)}%')\n",
        "print(f'긍정 = {round(y_train.value_counts()[1]/len(y_train) * 100,3)}%')\n",
        "print(f'부정 = {round(y_train.value_counts()[2]/len(y_train) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-gTg1Z2kYK2",
        "outputId": "c2fdae1d-5d48-47cd-c0c5-df97e191a641"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------훈련 데이터의 비율-----------\n",
            "중립 = 59.354%\n",
            "긍정 = 28.165%\n",
            "부정 = 12.481%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('--------테스트 데이터의 비율-----------')\n",
        "print(f'중립 = {round(y_test.value_counts()[0]/len(y_test) * 100,3)}%')\n",
        "print(f'긍정 = {round(y_test.value_counts()[1]/len(y_test) * 100,3)}%')\n",
        "print(f'부정 = {round(y_test.value_counts()[2]/len(y_test) * 100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6E_XYZIVkcP3",
        "outputId": "83f54be2-aeba-42c3-d0be-6f6447086a32"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------테스트 데이터의 비율-----------\n",
            "중립 = 59.401%\n",
            "긍정 = 28.099%\n",
            "부정 = 12.5%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "X_train_encoded = tokenizer.texts_to_sequences(X_train)\n",
        "X_test_encoded = tokenizer.texts_to_sequences(X_test)\n",
        "print(X_train_encoded[:5])\n",
        "print(X_test_encoded[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qCRNyvpkjEY",
        "outputId": "9afc543f-0f4a-4b73-887a-853a1b265279"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[981, 9, 587, 12, 1, 249, 196, 3, 70], [982, 1, 1397, 99, 794, 3, 350, 380, 9, 60, 1561, 1, 254, 1049, 4468, 59, 4469, 263, 3, 1, 1143, 2, 2435, 90, 1050, 4, 1144, 105, 161, 254, 4470], [1, 3088, 9, 3, 2025, 1051, 1, 121, 2, 1, 365, 186, 351, 454, 4, 4471, 1, 3088, 5, 624, 407, 66, 852, 17, 1, 351, 454, 472, 12, 216, 41, 53], [1, 222, 2436, 9, 3089, 3, 6, 269, 4472, 3090, 15, 6, 4473, 983], [70, 984, 80, 503, 4474, 1398, 162, 29, 3, 14, 65, 52, 266, 12, 705, 26, 985, 3, 14, 2437, 48, 3, 352, 59, 9, 2438, 1745, 217, 1, 65, 52]]\n",
            "[[38, 2, 1, 2636, 1097, 17, 1, 723, 259, 19, 6, 277, 3045, 9, 2512, 2, 19, 4061], [451, 4453, 33, 687, 3, 2149, 3, 1, 1440, 2, 4453, 284, 4, 9, 106, 2, 451, 153, 504, 394, 243, 521, 3, 1, 686, 2398, 534], [1, 883, 9, 1451, 24, 8596, 26, 338, 853, 4, 64, 55, 3, 35], [1, 50, 60, 16, 6, 541, 175, 397, 3, 95, 1234, 7949], [3, 376, 981, 165, 6, 142, 31, 379, 81, 7, 1, 2753]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_to_index = tokenizer.word_index\n",
        "vocab_size = len(word_to_index) + 1\n",
        "print(vocab_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAXqMlPtkm9d",
        "outputId": "1ac80e3d-5f9a-42d3-9fda-a10f8d75b3f1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9348\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('본문의 최대 길이 :',max(len(sent) for sent in X_train))\n",
        "print('본문의 평균 길이 :',sum(map(len, X_train))/len(X_train))\n",
        "plt.hist([len(sent) for sent in X_train], bins=50)\n",
        "plt.xlabel('length of samples')\n",
        "plt.ylabel('number of samples')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "CVaCLg2glTIo",
        "outputId": "d6eb433b-b22e-4995-dbe4-6d6d1e0e4313"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "본문의 최대 길이 : 302\n",
            "본문의 평균 길이 : 125.91602067183463\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len = 302"
      ],
      "metadata": {
        "id": "vZvzv8jClpTF"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_encoded = pad_sequences(X_train_encoded, maxlen=max_len)\n",
        "X_test_encoded = pad_sequences(X_test_encoded, maxlen=max_len)"
      ],
      "metadata": {
        "id": "DO-ypzo6lmuu"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_encoded.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3LInCb9lqUZ",
        "outputId": "8d554bd4-3ecd-45bd-ca22-289ff0c463e3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3870, 302)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_encoded.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRNfINkFlxHI",
        "outputId": "b79a5915-d79a-4896-a246-5dd3946408ca"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(968, 302)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "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.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint"
      ],
      "metadata": {
        "id": "wR9qe8oUlyKo"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)"
      ],
      "metadata": {
        "id": "OG8mNRPqmRb1"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_dim = 64\n",
        "hidden_units = 64\n",
        "num_classes = 3\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, embedding_dim))\n",
        "model.add(LSTM(hidden_units))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "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)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
        "history = model.fit(X_train_encoded, y_train, epochs=15, callbacks=[es, mc], batch_size=32, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qcgxpi4zmWHd",
        "outputId": "7aad670a-4a59-4788-9cff-f4f33fa8d825"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.8860 - acc: 0.6134\n",
            "Epoch 00001: val_acc improved from -inf to 0.64083, saving model to best_model.h5\n",
            "97/97 [==============================] - 6s 20ms/step - loss: 0.8860 - acc: 0.6134 - val_loss: 0.7715 - val_acc: 0.6408\n",
            "Epoch 2/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.6097 - acc: 0.7364\n",
            "Epoch 00002: val_acc improved from 0.64083 to 0.71318, saving model to best_model.h5\n",
            "97/97 [==============================] - 1s 15ms/step - loss: 0.6097 - acc: 0.7364 - val_loss: 0.7035 - val_acc: 0.7132\n",
            "Epoch 3/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.3214 - acc: 0.8915\n",
            "Epoch 00003: val_acc improved from 0.71318 to 0.71705, saving model to best_model.h5\n",
            "97/97 [==============================] - 1s 15ms/step - loss: 0.3214 - acc: 0.8915 - val_loss: 0.7717 - val_acc: 0.7171\n",
            "Epoch 4/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.1348 - acc: 0.9587\n",
            "Epoch 00004: val_acc improved from 0.71705 to 0.75194, saving model to best_model.h5\n",
            "97/97 [==============================] - 1s 15ms/step - loss: 0.1348 - acc: 0.9587 - val_loss: 0.8421 - val_acc: 0.7519\n",
            "Epoch 5/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.0715 - acc: 0.9800\n",
            "Epoch 00005: val_acc did not improve from 0.75194\n",
            "97/97 [==============================] - 1s 15ms/step - loss: 0.0715 - acc: 0.9800 - val_loss: 1.0336 - val_acc: 0.7209\n",
            "Epoch 6/15\n",
            "97/97 [==============================] - ETA: 0s - loss: 0.0508 - acc: 0.9871\n",
            "Epoch 00006: val_acc did not improve from 0.75194\n",
            "97/97 [==============================] - 1s 15ms/step - loss: 0.0508 - acc: 0.9871 - val_loss: 1.0690 - val_acc: 0.7158\n",
            "Epoch 00006: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = load_model('best_model.h5')\n",
        "print(\"\\n 테스트 정확도: %.4f\" % (loaded_model.evaluate(X_test_encoded, y_test)[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtj0EMOEmeEX",
        "outputId": "98c90e50-a33e-42ef-e056-91bdfb66a088"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31/31 [==============================] - 1s 6ms/step - loss: 0.7272 - acc: 0.7738\n",
            "\n",
            " 테스트 정확도: 0.7738\n"
          ]
        }
      ]
    }
  ]
}