sentiment analysis using ann.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26c887c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0fd41e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>message to examine</th>\n",
       "      <th>label (depression result)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106</td>\n",
       "      <td>just had a real good moment. i missssssssss hi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>217</td>\n",
       "      <td>is reading manga  http://plurk.com/p/mzp1e</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>220</td>\n",
       "      <td>@comeagainjen http://twitpic.com/2y2lx - http:...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>288</td>\n",
       "      <td>@lapcat Need to send 'em to my accountant tomo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>540</td>\n",
       "      <td>ADD ME ON MYSPACE!!!  myspace.com/LookThunder</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                                 message to examine  \\\n",
       "0    106  just had a real good moment. i missssssssss hi...   \n",
       "1    217         is reading manga  http://plurk.com/p/mzp1e   \n",
       "2    220  @comeagainjen http://twitpic.com/2y2lx - http:...   \n",
       "3    288  @lapcat Need to send 'em to my accountant tomo...   \n",
       "4    540      ADD ME ON MYSPACE!!!  myspace.com/LookThunder   \n",
       "\n",
       "   label (depression result)  \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('sentiment.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a330ce90",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfid=TfidfVectorizer(max_features=5000)\n",
    "x=tfid.fit_transform(df['message to examine'])\n",
    "y=df['label (depression result)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a44fd8aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "41d7625c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8188dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=keras.Sequential([\n",
    "    keras.layers.Dense(128,activation='relu',input_shape=(x_train.shape[1],)),\n",
    "    keras.layers.Dense(64,activation='relu'),\n",
    "    keras.layers.Dense(64,activation='relu'),\n",
    "    keras.layers.Dense(64,activation='relu'),\n",
    "    keras.layers.Dense(1,activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b98ac89",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64eb8abd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "258/258 [==============================] - 1s 5ms/step - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.1880 - val_accuracy: 0.9782\n",
      "Epoch 2/5\n",
      "258/258 [==============================] - 1s 5ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1861 - val_accuracy: 0.9782\n",
      "Epoch 3/5\n",
      "258/258 [==============================] - 1s 5ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1964 - val_accuracy: 0.9787\n",
      "Epoch 4/5\n",
      "258/258 [==============================] - 1s 5ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.1897 - val_accuracy: 0.9777\n",
      "Epoch 5/5\n",
      "258/258 [==============================] - 1s 5ms/step - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.2013 - val_accuracy: 0.9792\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(x_train,y_train,epochs=5,batch_size=32,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dacee771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65/65 [==============================] - 0s 902us/step - loss: 0.2013 - accuracy: 0.9792\n",
      "Test loss: 0.201257\n",
      "Test Acciracy: 0.979157\n"
     ]
    }
   ],
   "source": [
    "test_loss,test_accuracy=model.evaluate(x_test,y_test)\n",
    "print(f\"Test loss: {test_loss:4f}\")\n",
    "print(f\"Test Acciracy: {test_accuracy:4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5103c1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"senti.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07cb126f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.engine.sequential.Sequential at 0x2d74d9b02e0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load=keras.models.load_model('senti.keras')\n",
    "load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d43f2f3b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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