Movie Review Analysis - Natural Language Processing

nlp.py

import numpy as np 
import pandas as pd 
import re
import nltk 
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer 
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split 
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import requests 
import os 

# Downloading the stopwords
nltk.download('stopwords') 

# Downloading the dataset
if not os.path.exists("Restaurant_Reviews.tsv"):
	r = requests.get('https://media.geeksforgeeks.org/wp-content/uploads/Restaurant_Reviews.tsv')
	with open("Restaurant_Reviews.tsv",'wb') as f:
		f.write(r.content)

dataset = pd.read_csv('Restaurant_Reviews.tsv',delimiter='\t')

# Cleaning the dataset

corpus = list()
ps = PorterStemmer()
stopwords = set(stopwords.words('english'))

for i in range(0,1000):
	review = re.sub('[^a-zA-Z]',' ',dataset['Review'][i]) # Replacing numbers with spaces 
	review = review.lower() # Converting all letters to lowercase
	review = review.split() 
	review = [ps.stem(word) for word in review if not word in stopwords] # Removing stopwords

	review = " ".join(review)
	corpus.append(review)

# Vectorizing the corpus data 

cv = CountVectorizer(max_features=1500)

# Preparing the dataset for training 
X = cv.fit_transform(corpus).toarray()

y = dataset.iloc[:,1].values 

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.25)

# Using Random Forest classifier for predictions 

model = RandomForestClassifier(n_estimators=501,criterion='entropy')

model.fit(X_train,y_train)

# Performing predictions
y_pred = model.predict(X_test)

print("Accuracy: ", (y_pred == y_test).sum() / len(y_test) * 100,"%")

# Generating confusion matrix
print("Confusion matrix:")

cols = ["Predicted No","Predicted Yes"]
rows = ["Actual No","Actual Yes"]

cm = confusion_matrix(y_test,y_pred)
df = pd.DataFrame(cm,columns=cols,index=rows)
print(df)

requirements.txt

nltk
numpy
pandas
sklearn
requests