natural_language_processing.py

# 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.csv', 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):
    #not removing characters that are not from a-z and A-Z
    #the remove character will be replaced with an empty space
    review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
    #this will put all the characters to lowercase character
    review = review.lower()
    review = review.split()
    #removing all unnecessary words that has no effect on the review algorithm 
    #e.g the, this, that,and, in, prepositons that will not be relevant in 
    #determining if the comment is a positive or negative review
    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
    
#the bag of words model is taking different and unique words from the 1000 reviews to simplify the words
# and generate a model with the number of times a certain word appears in the review
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
#this model is now outdated) - from sklearn.cross_validation import train_test_split
from sklearn.model_selection 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)