Simple sentiment analysis and pply LSTM model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f16bbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Embedding,LSTM\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e144c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10309</th>\n",
       "      <td>802309</td>\n",
       "      <td>No Depression by G Herbo is my mood from now o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10310</th>\n",
       "      <td>802310</td>\n",
       "      <td>What do you do when depression succumbs the br...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10311</th>\n",
       "      <td>802311</td>\n",
       "      <td>Ketamine Nasal Spray Shows Promise Against Dep...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10312</th>\n",
       "      <td>802312</td>\n",
       "      <td>dont mistake a bad day with depression! everyo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10313</th>\n",
       "      <td>802313</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10314 rows × 3 columns</p>\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",
       "10309  802309  No Depression by G Herbo is my mood from now o...   \n",
       "10310  802310  What do you do when depression succumbs the br...   \n",
       "10311  802311  Ketamine Nasal Spray Shows Promise Against Dep...   \n",
       "10312  802312  dont mistake a bad day with depression! everyo...   \n",
       "10313  802313                                                  0   \n",
       "\n",
       "       label (depression result)  \n",
       "0                              0  \n",
       "1                              0  \n",
       "2                              0  \n",
       "3                              0  \n",
       "4                              0  \n",
       "...                          ...  \n",
       "10309                          1  \n",
       "10310                          1  \n",
       "10311                          1  \n",
       "10312                          1  \n",
       "10313                          1  \n",
       "\n",
       "[10314 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('sentiment.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3ebd073",
   "metadata": {},
   "outputs": [],
   "source": [
    "texts=df['message to examine'].values\n",
    "labels=df['label (depression result)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f805647",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer=Tokenizer()\n",
    "tokenizer.fit_on_texts(texts)\n",
    "sequences=tokenizer.texts_to_sequences(texts)\n",
    "data=pad_sequences(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd28da46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step3:define ant rain lstm model\n",
    "x_train,x_test,y_train,y_test=train_test_split(data,labels,test_size=0.2)\n",
    "model=Sequential()\n",
    "model.add(Embedding(len(tokenizer.word_index)+1,32,input_length=data.shape[1]))\n",
    "model.add(LSTM(64))\n",
    "\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9732bb26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=np.asarray(y_train,dtype=float)\n",
    "y_test=np.asarray(y_test,dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a26ab87b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258/258 [==============================] - 12s 41ms/step - loss: 0.2169 - accuracy: 0.9124 - val_loss: 0.0264 - val_accuracy: 0.9956\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x25ed7befca0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f9e211b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 363ms/step\n"
     ]
    }
   ],
   "source": [
    "new_text=\"I had a terrible experiance with product and service\"\n",
    "new_sequence=tokenizer.texts_to_sequences([new_text])\n",
    "new_data=pad_sequences(new_sequence,maxlen=data.shape[1])\n",
    "predicted_semtiment=model.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "471ed3e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65/65 [==============================] - 1s 11ms/step - loss: 0.0264 - accuracy: 0.9956\n",
      "Accuracy: 0.9956374168395996\n"
     ]
    }
   ],
   "source": [
    "scores=model.evaluate(x_test,y_test)\n",
    "print('Accuracy:',scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0947a6d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}