<reponame>kumaranu7/NLP-of-Restaurants-Reviews
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
import re

import nltk
nltk.download('stopwords') 
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
''' an empty string corpus defined'''
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)
    '''adding and updating the corpus value everytime'''
 #creating bag of words, as we are doing classification basically
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray() #since X is a matrix so we converted into an array.
'''we get a lot of zeros so we can use CountVectorizer(max_features) or dimensionality reduction'''
y = dataset.iloc[:,1].values
#now we will train our model, generally we use Naives Bayes, Decision Tree Classification, Random Forest Classification
#here we'll use Naive Bayes
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)

# Feature Scaling
'''from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)'''

# Create your classifier here
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)
  



import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
import re

import nltk
nltk.download('stopwords') 
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
''' an empty string corpus defined'''
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)
    '''adding and updating the corpus value everytime'''
 #creating bag of words, as we are doing classification basically
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features = 1500)
X = cv.fit_transform(corpus).toarray() #since X is a matrix so we converted into an array.
'''we get a lot of zeros so we can use CountVectorizer(max_features) or dimensionality reduction'''
y = dataset.iloc[:,1].values
#now we will train our model, generally we use Naives Bayes, Decision Tree Classification, Random Forest Classification
#here we'll use Naive Bayes
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)

# Feature Scaling
'''from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)'''

# Create your classifier here
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)
an empty string corpus defined adding and updating the corpus value everytime #creating bag of words, as we are doing classification basically #since X is a matrix so we converted into an array. we get a lot of zeros so we can use CountVectorizer(max_features) or dimensionality reduction #now we will train our model, generally we use Naives Bayes, Decision Tree Classification, Random Forest Classification #here we'll use Naive Bayes # Feature Scaling from sklearn.preprocessing import StandardScaler
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test) # Create your classifier here # Predicting the Test set results # Making the Confusion Matrix