nn_sentiment.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/efeecllk/investor-sentiments/blob/main/nn_sentiment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import gensim\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Input\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import nltk\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt_tab')\n"
      ],
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        "id": "46KjZwvmEJ9M",
        "outputId": "0e8ac025-99cd-4b35-8c56-9c410e9de081"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
          ]
        },
        {
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          "data": {
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              "True"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/NLP/clean_tweet.csv')\n"
      ],
      "metadata": {
        "id": "k1rWZhU7EuZM"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df_label_0 = df[df['label'] == 0].sample(n=4000, random_state=42)\n",
        "df_label_1 = df[df['label'] == 1].sample(n=4000, random_state=42)\n",
        "df_label_2 = df[df['label'] == 2].sample(n=4000, random_state=42)\n",
        "\n",
        "clean_df = pd.concat([df_label_0, df_label_1, df_label_2]).reset_index(drop=True)\n",
        "\n",
        "print(clean_df['label'].value_counts())\n"
      ],
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        },
        "id": "-GZDdURELf3y",
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      "outputs": [
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          "name": "stdout",
          "text": [
            "label\n",
            "0    4000\n",
            "1    4000\n",
            "2    4000\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_counts = clean_df['label'].value_counts()\n",
        "print(label_counts)\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "KVj7QgzOLKNX",
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      "outputs": [
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          "name": "stdout",
          "text": [
            "label\n",
            "2    10000\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_df.info()\n"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12000 entries, 0 to 11999\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text_v  12000 non-null  object\n",
            " 1   label   12000 non-null  int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 187.6+ KB\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "clean_df.head()"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              text_v  label\n",
              "0  financial expert predict steady increase value...      0\n",
              "1  coin experiencing surge trading volume today c...      0\n",
              "2  blackrock announces dividend payment dividend ...      0\n",
              "3  investor urged exercise caution face market fl...      0\n",
              "4  financial technology fintech rapidly transform...      0"
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              "summary": "{\n  \"name\": \"clean_df\",\n  \"rows\": 12000,\n  \"fields\": [\n    {\n      \"column\": \"text_v\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11997,\n        \"samples\": [\n          \"recent study major financial institution indicates stock market volatility may decrease near future providing potential opportunity longterm investor\",\n          \"f coiling hard right\",\n          \"rumor potential amzn acquisition hype wont impact market investmenthoax\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    {
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      "execution_count": null,
      "metadata": {
        "id": "jTdf8qe9Du92"
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      "outputs": [],
      "source": [
        "word2vec_model = gensim.models.Word2Vec.load('/content/drive/MyDrive/NLP/word2vec_model.model')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(word2vec_model.wv['good'] if 'good' in word2vec_model.wv else \"Kelime bulunamadı\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TD-n0Ra9D55D",
        "outputId": "e5b06206-8180-4a8d-d605-4e7ffcfe884a"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[ 1.2065066  -1.5498743   4.3290806   1.0235827  -2.1191995  -1.91932\n",
            " -2.627178   -2.0179203   2.7778153   0.6003324  -1.7520316  -1.2726218\n",
            " -0.38420677  0.4580238  -0.74335617  2.7906876  -1.2787054   4.1497583\n",
            " -4.2355137  -0.0926513   2.2251964  -3.625772   -4.4894533  -1.553813\n",
            "  1.0811808   2.416639   -0.82610786 -3.0229242  -2.5270538   1.2096163\n",
            " -2.809983   -1.0944238   0.86705756 -0.3064526   3.830741   -0.86024964\n",
            "  0.6256468   2.1056542   1.5155813  -1.5912137  -1.600227   -2.2993665\n",
            "  2.0102804   0.43171695  0.0521418   0.7106995  -1.5751389   2.0218503\n",
            "  1.6405704   0.79498047  0.5910279  -1.612377    3.2804937   0.7853499\n",
            " -0.21600401 -3.304779   -0.56850135 -0.5327709   0.19682382  1.2346387\n",
            " -0.6314158   2.6147592   0.31772268  1.0094956   1.1704543  -0.82502985\n",
            " -3.0374503  -2.0160258   0.9556052   2.9681206   1.0318193   2.0981019\n",
            " -1.7339318  -2.8082595   1.6445243   0.28596678 -0.31484386 -0.9602643\n",
            " -2.755997   -6.159275   -0.16718331 -1.1541643  -0.9273664   0.28406185\n",
            " -0.55561936  2.3298845   2.3990576  -0.3172749  -0.4118589   2.2541611\n",
            "  2.6206994  -2.6051223  -3.0191119   3.2831628   1.7298458   0.7315454\n",
            " -2.526264    3.5910594  -1.6319789   0.10393571]\n"
          ]
        }
      ]
    },
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      "source": [
        "def preprocess_text(text):\n",
        "    # Ensure the input is a string\n",
        "    if not isinstance(text, str):\n",
        "        text = str(text)  # Convert to string if not already\n",
        "    tokens = word_tokenize(text)\n",
        "    tokens = [word for word in tokens if word not in stopwords.words('english')]\n",
        "    return tokens\n",
        "\n",
        "def text_to_word2vec(text, model):\n",
        "    tokens = preprocess_text(text)\n",
        "    # Accessing KeyedVectors using model.wv\n",
        "    word_vectors = [model.wv[word] for word in tokens if word in model.wv.key_to_index]\n",
        "    if len(word_vectors) == 0:\n",
        "        return np.zeros(model.vector_size)\n",
        "    return np.mean(word_vectors, axis=0)"
      ],
      "metadata": {
        "id": "V46G424fD7XV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clean_df['text_vector'] = clean_df['text_v'].apply(lambda x: text_to_word2vec(x, word2vec_model))\n",
        "\n",
        "clean_df[['text_v', 'text_vector']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "4mfHSaLFKahT",
        "outputId": "2c1b06f9-3ac7-4605-928b-c9418e233657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              text_v  \\\n",
              "0  financial expert predict steady increase value...   \n",
              "1  coin experiencing surge trading volume today c...   \n",
              "2  blackrock announces dividend payment dividend ...   \n",
              "3  investor urged exercise caution face market fl...   \n",
              "4  financial technology fintech rapidly transform...   \n",
              "\n",
              "                                         text_vector  \n",
              "0  [-0.4238628, 1.3990093, -0.4794796, -1.5149565...  \n",
              "1  [-0.2896792, 1.2717527, -0.8889524, -0.7738165...  \n",
              "2  [0.080770425, -0.027151993, 0.939373, -0.81429...  \n",
              "3  [-0.2745082, 0.67496586, 1.818446, -1.5813758,...  \n",
              "4  [-2.4542024, 0.48817265, -0.68885916, -0.38548...  "
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"clean_df[['text_v', 'text_vector']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text_v\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"coin experiencing surge trading volume today company announces new partnership major financial institution\",\n          \"financial technology fintech rapidly transforming traditional banking practice\",\n          \"blackrock announces dividend payment dividend investment\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_vector\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Extract features and labels\n",
        "X = np.vstack(clean_df['text_vector'].values)\n",
        "y = clean_df['label']\n",
        "\n",
        "# Encode labels (Bullish=1, Bearish=2, Neutral=0)\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n",
        "\n",
        "print(\"Dataset prepared successfully!\")\n",
        "print(f\"Train set shape: {X_train.shape}, Test set shape: {X_test.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDi0-bZYFITk",
        "outputId": "8a71fff3-7360-43ad-a7b8-d2f0e747932d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset prepared successfully!\n",
            "Train set shape: (9600, 100), Test set shape: (2400, 100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Define NN model\n",
        "model = Sequential([\n",
        "    Input(shape=(word2vec_model.vector_size,)),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(3, activation='softmax')  # 3 classes: Bullish, Bearish, Neutral\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "1YjbzYE1K5f0",
        "outputId": "1bf9d2d1-fa0b-49ec-bfc8-257a22e9ff46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │          \u001b[38;5;34m12,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │           \u001b[38;5;34m8,256\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │             \u001b[38;5;34m195\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">12,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m21,379\u001b[0m (83.51 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">21,379</span> (83.51 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m21,379\u001b[0m (83.51 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">21,379</span> (83.51 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    validation_split=0.2,\n",
        "    epochs=20,\n",
        "    batch_size=32,\n",
        "    verbose=1\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9CCx_fQK8in",
        "outputId": "e1ca4ce1-829b-49f2-ad20-c664319b2c89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8929 - loss: 0.2778 - val_accuracy: 0.8568 - val_loss: 0.3706\n",
            "Epoch 2/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8889 - loss: 0.2721 - val_accuracy: 0.8505 - val_loss: 0.3835\n",
            "Epoch 3/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9007 - loss: 0.2541 - val_accuracy: 0.8552 - val_loss: 0.3719\n",
            "Epoch 4/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9014 - loss: 0.2656 - val_accuracy: 0.8521 - val_loss: 0.3847\n",
            "Epoch 5/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8995 - loss: 0.2533 - val_accuracy: 0.8578 - val_loss: 0.3906\n",
            "Epoch 6/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9080 - loss: 0.2313 - val_accuracy: 0.8469 - val_loss: 0.3989\n",
            "Epoch 7/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9174 - loss: 0.2133 - val_accuracy: 0.8562 - val_loss: 0.4216\n",
            "Epoch 8/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9105 - loss: 0.2139 - val_accuracy: 0.8531 - val_loss: 0.3956\n",
            "Epoch 9/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9192 - loss: 0.2118 - val_accuracy: 0.8557 - val_loss: 0.4048\n",
            "Epoch 10/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9262 - loss: 0.1977 - val_accuracy: 0.8479 - val_loss: 0.4205\n",
            "Epoch 11/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9252 - loss: 0.1960 - val_accuracy: 0.8547 - val_loss: 0.4202\n",
            "Epoch 12/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9268 - loss: 0.1838 - val_accuracy: 0.8573 - val_loss: 0.4154\n",
            "Epoch 13/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9224 - loss: 0.1969 - val_accuracy: 0.8547 - val_loss: 0.4374\n",
            "Epoch 14/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9298 - loss: 0.1877 - val_accuracy: 0.8557 - val_loss: 0.4508\n",
            "Epoch 15/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9281 - loss: 0.1821 - val_accuracy: 0.8479 - val_loss: 0.4524\n",
            "Epoch 16/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9375 - loss: 0.1718 - val_accuracy: 0.8510 - val_loss: 0.4446\n",
            "Epoch 17/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9359 - loss: 0.1649 - val_accuracy: 0.8589 - val_loss: 0.4305\n",
            "Epoch 18/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9408 - loss: 0.1580 - val_accuracy: 0.8484 - val_loss: 0.4588\n",
            "Epoch 19/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9415 - loss: 0.1554 - val_accuracy: 0.8500 - val_loss: 0.4515\n",
            "Epoch 20/20\n",
            "\u001b[1m240/240\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9408 - loss: 0.1485 - val_accuracy: 0.8469 - val_loss: 0.5083\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Model\n",
        "\n",
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f\"Test Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Plot training history\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "x9R1gk3wMC2l",
        "outputId": "866dd780-f5b8-469d-8140-41fc33e08ce2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m75/75\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8644 - loss: 0.3310\n",
            "Test Accuracy: 0.86\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the Trained Model\n",
        "\n",
        "model.save('/content/drive/MyDrive/NLP/sentiment_nn_model.h5')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fl7wDBfOMUtD",
        "outputId": "91bc2f53-910a-4f4d-e6f6-e130f19033c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Neural Network Sentiment Model saved successfully!\n"
          ]
        }
      ]
    }
  ]
}