#------------------------------------------------- Natural Language Processing (NLP) -------------------------------------------------#


#---Libraries Import---#
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#---Importing the dataset---#
dataset = pd.read_csv('FileName.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()
    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
    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.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

#---Fitting Naive Bayes to 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)

#---Making the Confusion Matrix---#
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
