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

# Importing 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 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 datset into training 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=0.2, random_state=0)

# Training the Naive Bayes Model
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(x_train, y_train)

# Predicting 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 Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
print(accuracy_score(y_test, y_pred))