Python - NLP Analysis of Restaurant Reviews

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of machine learning which is about analyzing any text and handling predictive analysis.

Scikit-learn is a free software machine learning library for Python programming language. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Cython is a superset of the Python programming language, designed to give C-like performance with code that is written mostly in Python.

For my project, I looked into Yelp reviews and use Natural Language Processing (NLP) to extract more meaning out of them.
Project Abstract

Let’s understand the various steps involved in text processing and the flow of NLP.

This algorithm can be easily applied to any other kind of text like classify book into like Romance, Friction, but for now, let’s use a restaurant review dataset to review negative or positive feedback.

Steps involved:

Step 1: Import dataset with setting delimiter as ‘\t’ as columns are separated as tab space. Reviews and their category(0 or 1) are not separated by any other symbol but with tab space as most of the other symbols are is the review (like $ for price, ….!, etc) and the algorithm might use them as delimiter, which will lead to strange behavior (like errors, weird output) in output.

# Importing Libraries 
import numpy as np 
import pandas as pd 

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

To download the Restaurant_Reviews.tsv dataset used, click here.

Step 2: Text Cleaning or Preprocessing

Remove Punctuations, Numbers: Punctuations, Numbers doesn’t help much in processong the given text, if included, they will just increase the size of bag of words that we will create as last step and decrase the efficency of algorithm.
Stemming: Take roots of the word
Convert each word into its lower case: For example, it useless to have same words in different cases (eg ‘good’ and ‘GOOD’).

# library to clean data 
import re 

# Natural Language Tool Kit 
import nltk 

nltk.download('stopwords') 

# to remove stopword 
from nltk.corpus import stopwords 

# for Stemming propose 
from nltk.stem.porter import PorterStemmer 

# Initialize empty array 
# to append clean text 
corpus = [] 

# 1000 (reviews) rows to clean 
for i in range(0, 1000): 
	
	# column : "Review", row ith 
	review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i]) 
	
	# convert all cases to lower cases 
	review = review.lower() 
	
	# split to array(default delimiter is " ") 
	review = review.split() 
	
	# creating PorterStemmer object to 
	# take main stem of each word 
	ps = PorterStemmer() 
	
	# loop for stemming each word 
	# in string array at ith row	 
	review = [ps.stem(word) for word in review 
				if not word in set(stopwords.words('english'))] 
				
	# rejoin all string array elements 
	# to create back into a string 
	review = ' '.join(review) 
	
	# append each string to create 
	# array of clean text 
	corpus.append(review)

Examples: Before and after applying above code (reviews = > before, corpus => after)

Step 3: Tokenization, involves splitting sentences and words from the body of the text.

Step 4: Making the bag of words via sparse matrix

Take all the different words of reviews in the dataset without repeating of words.
One column for each word, therefore there are going to be many columns.
Rows are reviews
If word is there in row of dataset of reviews, then the count of word will be there in row of bag of words under the column of the word.

Examples: Let’s take a dataset of reviews of only two reviews

Input : "dam good steak", "good food good servic"
Output :
 ![](https://media.geeksforgeeks.org/wp-content/uploads/eg.png)

For this purpose we need CountVectorizer class from sklearn.feature_extraction.text.
We can also set a max number of features (max no. features which help the most via attribute “max_features”). Do the training on the corpus and then apply the same transformation to the corpus “.fit_transform(corpus)” and then convert it into an array. If review is positive or negative that answer is in the second column of the dataset[:, 1] : all rows and 1st column (indexing from zero).

# Creating the Bag of Words model 
from sklearn.feature_extraction.text import CountVectorizer 

# To extract max 1500 feature. 
# "max_features" is attribute to 
# experiment with to get better results 
cv = CountVectorizer(max_features = 1500) 

# X contains corpus (dependent variable) 
X = cv.fit_transform(corpus).toarray() 

# y contains answers if review 
# is positive or negative 
y = dataset.iloc[:, 1].values

Description of the dataset to be used:

Columns seperated by \t (tab space)

First column is about reviews of people

In second column, 0 is for negative review and 1 is for positive review

Step 5 : Splitting Corpus into Training and Test set. For this, we need class train_test_split from sklearn.cross_validation. Split can be made 70/30 or 80/20 or 85/15 or 75/25, here I choose 75/25 via “test_size”.
X is the bag of words, y is 0 or 1 (positive or negative).

# Splitting the dataset into 
# the Training set and Test set 
from sklearn.cross_validation import train_test_split 

# experiment with "test_size" 
# to get better results 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)

Step 6: Fitting a Predictive Model (here random forest)

Since Random fored is ensemble model (made of many trees) from sklearn.ensemble, import RandomForestClassifier class
With 501 tree or “n_estimators” and criterion as ‘entropy’
Fit the model via .fit() method with attributes X_train and y_train

# Fitting Random Forest Classification 
# to the Training set 
from sklearn.ensemble import RandomForestClassifier 

# n_estimators can be said as number of 
# trees, experiment with n_estimators 
# to get better results 
model = RandomForestClassifier(n_estimators = 501, 
							criterion = 'entropy') 
							
model.fit(X_train, y_train)

Step 7: Pridicting Final Results via using .predict() method with attribute X_test

# Predicting the Test set results 
y_pred = model.predict(X_test) 

y_pred

Note: Accuracy with random forest was 72%.(It may be different when performed experiment with different test size, here = 0.25).

Step 8: To know the accuracy, confusion matrix is needed.

Confusion Matrix is a 2X2 Matrix.

TRUE POSITIVE : measures the proportion of actual positives that are correctly identified.
TRUE NEGATIVE : measures the proportion of actual positives that are not correctly identified.
FALSE POSITIVE : measures the proportion of actual negatives that are correctly identified.
FALSE NEGATIVE : measures the proportion of actual negatives that are not correctly identified.

Note : True or False refers to the assigned classification being Correct or Incorrect, while Positive or Negative refers to assignment to the Positive or the Negative Category

# Making the Confusion Matrix 
from sklearn.metrics import confusion_matrix 

cm = confusion_matrix(y_test, y_pred) 

cm

Thank for reading !

#Python #NLP #Data Science #Mongodb