project2.py

from math import ceil
from tabnanny import verbose
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras import layers
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences

def run():


    filepath=r'C:\Users\DELL\Desktop\Ajay\Text classification\sentiment labelled sentences\feedbacks.txt'
    df=pd.read_csv(filepath,names=['sentence','label'],sep='\t')
    sentences=df['sentence'].values
    y=df['label'].values
    sentences_train,sentences_test,y_train,y_test=train_test_split(sentences,y,test_size=0.25,random_state=1000)

    vectorizer=CountVectorizer(min_df=0,lowercase=False)
    vectorizer.fit(sentences_train)
    x_train=vectorizer.transform(sentences_train)
    x_test=vectorizer.transform(sentences_test)

    classifier=LogisticRegression()
    classifier.fit(x_train,y_train)
    score=classifier.score(x_test,y_test)

    tokenizer=Tokenizer(num_words=500)
    tokenizer.fit_on_texts(sentences_train)
    x_train=tokenizer.texts_to_sequences(sentences_train)
    x_test=tokenizer.texts_to_sequences(sentences_test)
    vocab_size=len(tokenizer.word_index)+1

    maxlen=100
    x_train=pad_sequences(x_train,padding='post',maxlen=maxlen)
    x_test=pad_sequences(x_test,padding='post',maxlen=maxlen)

    embedding_dim=50
    
    model=Sequential()
    model.add(layers.Embedding(input_dim=vocab_size,output_dim=embedding_dim,input_length=maxlen))
    model.add(layers.Conv1D(128,5,activation='relu'))
    model.add(layers.GlobalMaxPool1D())
    model.add(layers.Dense(10,activation='relu'))
    model.add(layers.Dense(1,activation='sigmoid'))
    model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
    history=model.fit(x_train,y_train,epochs=20,verbose=False,validation_data=(x_test,y_test),batch_size=10)
    loss,accuracy=model.evaluate(x_train,y_train,verbose=False)
    print(loss,accuracy)
    loss,accuracy=model.evaluate(x_test,y_test,verbose=False)
    print(loss,accuracy)
    model.save_weights('model.h5')
    s=0
    c=0
    print("Enter your review")
    while True:
        st=input()
        if(st==""):
            break
        l=[]
        l.append(st)
        test=tokenizer.texts_to_sequences(l)
        test=pad_sequences(test,padding='post',maxlen=maxlen)
        l=model.predict(test)
        print(ceil(l[0][0]*5))
        s=s+ceil(l[0][0]*5)
        c=c+1
    print(s/c)
run()