#Natural Language Processing

#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)

#Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
corpus = []
for i in range(0,1000):
    review  = re.sub('[^a-zA-Z]', ' ',dataset['Review'][i])
    review = review.lower()
    review = review.split()
    ps = PorterStemmer()
    all_stopwords = stopwords.words('english');
    all_stopwords.remove('not') 
    review = [ps.stem(word) for word in review if not word in set(all_stopwords)]
    review = ' '.join(review)
    corpus.append(review)

#Creating the Bag of Words Model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray()
Y = dataset.iloc[:,-1].values


#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.25,random_state=0)

#Training the Naive Bayes model on the Training Set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train,Y_train)

#Predicting the Test set Results
Y_pred=classifier.predict(X_test)
print(np.concatenate((Y_pred.reshape(len(Y_pred),1),Y_test.reshape(len(Y_test),1)),1))

#Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm=confusion_matrix(Y_test, Y_pred)
print(cm)
accuracy_score(Y_test, Y_pred)

