neuralnetwork (1).ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "**IMPORTING LIBRARIES**"
      ],
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        "id": "GkKXfZUVV2v_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OGCe52ldijdK"
      },
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      "source": [
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras import Input\n",
        "from keras.layers import Dense\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATASET INFORMATION**"
      ],
      "metadata": {
        "id": "-lR8fNmMV8hK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/extracted.csv', encoding='latin1')\n",
        "df"
      ],
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              "       Unnamed: 0                                    processed__text  \\\n",
              "0               0                                    responded going   \n",
              "1               1                            sooo sad miss san diego   \n",
              "2               2                                       bos bullying   \n",
              "3               3                              interview leave alone   \n",
              "4               4                     son put release already bought   \n",
              "...           ...                                                ...   \n",
              "27475       27476  wish could come see u denver husband lost job ...   \n",
              "27476       27477  wondered rake client made clear force devs lea...   \n",
              "27477       27478  yay good enjoy break probably need hectic week...   \n",
              "27478       27479                                              worth   \n",
              "27479       27480                   flirting going atg smile yay hug   \n",
              "\n",
              "       Positive Words  Negative Words  Neutral Words  sentiment  \n",
              "0                 0.0             0.0            2.0          1  \n",
              "1                 0.0             1.0            4.0          0  \n",
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              "27479             3.0             0.0            3.0          1  \n",
              "\n",
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              "      <td>yay good enjoy break probably need hectic week...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>27479</td>\n",
              "      <td>worth</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>3.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 95
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRjv91EWjXkX",
        "outputId": "6899676a-dc98-4930-c501-0a599b239c6e"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0           0\n",
              "processed__text    105\n",
              "Positive Words     105\n",
              "Negative Words     105\n",
              "Neutral Words      105\n",
              "sentiment            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.dropna()"
      ],
      "metadata": {
        "id": "twlqrEHOjfZ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0Ut9ZvdjiCZ",
        "outputId": "2afe7861-3f16-4a2d-d9d8-cdc465b6d676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "processed__text    0\n",
              "Positive Words     0\n",
              "Negative Words     0\n",
              "Neutral Words      0\n",
              "sentiment          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woMX_nVhjk61",
        "outputId": "fee0f507-d380-4f10-9b34-02f92acd0dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27375, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Positive Words','Negative Words','Neutral Words']]\n",
        "y= df[['sentiment']]"
      ],
      "metadata": {
        "id": "zDU5srLy4hhp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = pd.get_dummies(df['sentiment'])\n"
      ],
      "metadata": {
        "id": "n_66z-aJAv0Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71fXajmTdBa9",
        "outputId": "351f1f44-bdb9-4541-a71a-362a8d5c474f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       0  1  2\n",
            "0      0  1  0\n",
            "1      1  0  0\n",
            "2      1  0  0\n",
            "3      1  0  0\n",
            "4      1  0  0\n",
            "...   .. .. ..\n",
            "27475  1  0  0\n",
            "27476  1  0  0\n",
            "27477  0  0  1\n",
            "27478  0  0  1\n",
            "27479  0  1  0\n",
            "\n",
            "[27375 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NEURAL NETWORK TRAINING**"
      ],
      "metadata": {
        "id": "iSKlx8tSWDkm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20,random_state=42)\n",
        "model = Sequential(name='Sentiment_Analysis_using_3_Input_Features')\n",
        "model.add(Input(shape=(3,), name='Input_Layer'))\n",
        "model.add(Dense(2, activation='relu', name='Hidden_Layer'))\n",
        "model.add(Dense(3, activation='softmax', name='Output_Layer'))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['Accuracy', 'Precision', 'Recall'],\n",
        "              loss_weights=None,\n",
        "              weighted_metrics=None,\n",
        "              run_eagerly=None,\n",
        "              steps_per_execution=None\n",
        "             )"
      ],
      "metadata": {
        "id": "JJr7L_mQ5Nbb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train,\n",
        "          batch_size=32,\n",
        "          epochs=25,\n",
        "          verbose='auto',\n",
        "          callbacks=None,\n",
        "          validation_split=0.2,\n",
        "          shuffle=True,\n",
        "          class_weight=None,\n",
        "          sample_weight=None,\n",
        "          initial_epoch=0,\n",
        "          steps_per_epoch=594,  # samples - (test_size * samples) / batch_size\n",
        "          validation_steps=None,\n",
        "          validation_batch_size=32,  # same as batch size\n",
        "          validation_freq=1,\n",
        "          max_queue_size=10,\n",
        "          workers=1,\n",
        "          use_multiprocessing=False)\n",
        "\n",
        "y_pred_probs = model.predict(X_test)\n",
        "y_pred_labels = np.argmax(y_pred_probs, axis=1)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7k6aq7NjnP7",
        "outputId": "d02a95c0-3709-4574-90d8-7397b4c9ae3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "594/594 [==============================] - 3s 4ms/step - loss: 1.2446 - Accuracy: 0.4137 - precision: 0.3923 - recall: 0.1472 - val_loss: 1.0409 - val_Accuracy: 0.4678 - val_precision: 0.5149 - val_recall: 0.0514\n",
            "Epoch 2/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 1.0193 - Accuracy: 0.4823 - precision: 0.5936 - recall: 0.0676 - val_loss: 1.0118 - val_Accuracy: 0.4692 - val_precision: 0.6161 - val_recall: 0.1048\n",
            "Epoch 3/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9927 - Accuracy: 0.4804 - precision: 0.6110 - recall: 0.2164 - val_loss: 0.9774 - val_Accuracy: 0.4820 - val_precision: 0.6524 - val_recall: 0.1256\n",
            "Epoch 4/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9590 - Accuracy: 0.4946 - precision: 0.6120 - recall: 0.2025 - val_loss: 0.9523 - val_Accuracy: 0.5210 - val_precision: 0.6634 - val_recall: 0.1247\n",
            "Epoch 5/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9454 - Accuracy: 0.5141 - precision: 0.6849 - recall: 0.1333 - val_loss: 0.9364 - val_Accuracy: 0.5217 - val_precision: 0.6891 - val_recall: 0.1215\n",
            "Epoch 6/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9337 - Accuracy: 0.5256 - precision: 0.7070 - recall: 0.1308 - val_loss: 0.9253 - val_Accuracy: 0.5139 - val_precision: 0.7194 - val_recall: 0.1183\n",
            "Epoch 7/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9259 - Accuracy: 0.5306 - precision: 0.7223 - recall: 0.1263 - val_loss: 0.9168 - val_Accuracy: 0.5308 - val_precision: 0.7191 - val_recall: 0.1180\n",
            "Epoch 8/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.9122 - Accuracy: 0.5698 - precision: 0.7237 - recall: 0.1274 - val_loss: 0.9094 - val_Accuracy: 0.6322 - val_precision: 0.7191 - val_recall: 0.1180\n",
            "Epoch 9/25\n",
            "594/594 [==============================] - 2s 3ms/step - loss: 0.9051 - Accuracy: 0.6246 - precision: 0.7261 - recall: 0.1266 - val_loss: 0.9032 - val_Accuracy: 0.6032 - val_precision: 0.7191 - val_recall: 0.1180\n",
            "Epoch 10/25\n",
            "594/594 [==============================] - 2s 3ms/step - loss: 0.9040 - Accuracy: 0.6259 - precision: 0.7459 - recall: 0.2112 - val_loss: 0.8983 - val_Accuracy: 0.6105 - val_precision: 0.7447 - val_recall: 0.2390\n",
            "Epoch 11/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8982 - Accuracy: 0.6170 - precision: 0.7484 - recall: 0.2424 - val_loss: 0.8942 - val_Accuracy: 0.6283 - val_precision: 0.7443 - val_recall: 0.2393\n",
            "Epoch 12/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8940 - Accuracy: 0.6296 - precision: 0.7482 - recall: 0.2457 - val_loss: 0.8918 - val_Accuracy: 0.6285 - val_precision: 0.7447 - val_recall: 0.2390\n",
            "Epoch 13/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8916 - Accuracy: 0.6249 - precision: 0.7456 - recall: 0.2445 - val_loss: 0.8885 - val_Accuracy: 0.6217 - val_precision: 0.7384 - val_recall: 0.2404\n",
            "Epoch 14/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8884 - Accuracy: 0.6294 - precision: 0.7452 - recall: 0.2461 - val_loss: 0.8864 - val_Accuracy: 0.6135 - val_precision: 0.7378 - val_recall: 0.2416\n",
            "Epoch 15/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8875 - Accuracy: 0.6253 - precision: 0.7505 - recall: 0.2482 - val_loss: 0.8846 - val_Accuracy: 0.6267 - val_precision: 0.7379 - val_recall: 0.2404\n",
            "Epoch 16/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8872 - Accuracy: 0.6276 - precision: 0.7455 - recall: 0.2467 - val_loss: 0.8842 - val_Accuracy: 0.6283 - val_precision: 0.7284 - val_recall: 0.2468\n",
            "Epoch 17/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8814 - Accuracy: 0.6249 - precision: 0.7424 - recall: 0.2539 - val_loss: 0.8825 - val_Accuracy: 0.6128 - val_precision: 0.7345 - val_recall: 0.2438\n",
            "Epoch 18/25\n",
            "594/594 [==============================] - 2s 3ms/step - loss: 0.8898 - Accuracy: 0.6212 - precision: 0.7390 - recall: 0.2516 - val_loss: 0.8832 - val_Accuracy: 0.6304 - val_precision: 0.7443 - val_recall: 0.2393\n",
            "Epoch 19/25\n",
            "594/594 [==============================] - 2s 3ms/step - loss: 0.8829 - Accuracy: 0.6273 - precision: 0.7469 - recall: 0.2475 - val_loss: 0.8814 - val_Accuracy: 0.6217 - val_precision: 0.7335 - val_recall: 0.2438\n",
            "Epoch 20/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8803 - Accuracy: 0.6235 - precision: 0.7326 - recall: 0.2766 - val_loss: 0.8814 - val_Accuracy: 0.6153 - val_precision: 0.7291 - val_recall: 0.2463\n",
            "Epoch 21/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8789 - Accuracy: 0.6248 - precision: 0.7397 - recall: 0.2667 - val_loss: 0.8806 - val_Accuracy: 0.6130 - val_precision: 0.7286 - val_recall: 0.2463\n",
            "Epoch 22/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8828 - Accuracy: 0.6289 - precision: 0.7385 - recall: 0.2713 - val_loss: 0.8812 - val_Accuracy: 0.6128 - val_precision: 0.6925 - val_recall: 0.4411\n",
            "Epoch 23/25\n",
            "594/594 [==============================] - 1s 2ms/step - loss: 0.8823 - Accuracy: 0.6243 - precision: 0.7211 - recall: 0.3420 - val_loss: 0.8804 - val_Accuracy: 0.6215 - val_precision: 0.7227 - val_recall: 0.3005\n",
            "Epoch 24/25\n",
            " 26/594 [>.............................] - ETA: 1s - loss: 0.9009 - Accuracy: 0.6118 - precision: 0.7245 - recall: 0.2308"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 14850 batches). You may need to use the repeat() function when building your dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "594/594 [==============================] - 0s 459us/step - loss: 0.9025 - Accuracy: 0.6125 - precision: 0.7294 - recall: 0.2292 - val_loss: 0.8804 - val_Accuracy: 0.6217 - val_precision: 0.7286 - val_recall: 0.2463\n",
            "172/172 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('-------------------- Model Summary --------------------')\n",
        "model.summary()\n",
        "print(\"\")\n",
        "\n",
        "print('---------- Evaluation on Training Data ----------')\n",
        "y_train_pred_probs = model.predict(X_train)\n",
        "y_train_pred_labels = np.argmax(y_train_pred_probs, axis=1)\n",
        "print(classification_report(np.argmax(y_train.values, axis=1), y_train_pred_labels))\n",
        "\n",
        "print('---------- Evaluation on Test Data ----------')\n",
        "y_test_pred_probs = model.predict(X_test)\n",
        "y_test_pred_labels = np.argmax(y_test_pred_probs, axis=1)\n",
        "print(classification_report(np.argmax(y_test.values, axis=1), y_test_pred_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uW4FIJ0zBRhB",
        "outputId": "faf6fe00-be14-4f47-8a15-bc24f26f6b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------- Model Summary --------------------\n",
            "Model: \"Sentiment_Analysis_using_3_Input_Features\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Hidden_Layer (Dense)        (None, 2)                 8         \n",
            "                                                                 \n",
            " Output_Layer (Dense)        (None, 3)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17\n",
            "Trainable params: 17\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "---------- Evaluation on Training Data ----------\n",
            "685/685 [==============================] - 1s 1ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.49      0.58      6237\n",
            "           1       0.57      0.73      0.64      8850\n",
            "           2       0.68      0.62      0.65      6813\n",
            "\n",
            "    accuracy                           0.63     21900\n",
            "   macro avg       0.65      0.61      0.62     21900\n",
            "weighted avg       0.64      0.63      0.62     21900\n",
            "\n",
            "---------- Evaluation on Test Data ----------\n",
            "172/172 [==============================] - 0s 1ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.50      0.58      1540\n",
            "           1       0.55      0.73      0.63      2170\n",
            "           2       0.70      0.61      0.65      1765\n",
            "\n",
            "    accuracy                           0.62      5475\n",
            "   macro avg       0.65      0.61      0.62      5475\n",
            "weighted avg       0.64      0.62      0.62      5475\n",
            "\n"
          ]
        }
      ]
    }
  ]
}