Python | NLP analysis of Restaurant reviews
Last Updated :
19 Sep, 2024
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 the 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.
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 a book into 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 the price, ....!, etc) and the algorithm might use them as a delimiter, which will lead to strange behavior (like errors, weird output) in output.
Python
# 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 don't help much in processing the given text, if included, they will just increase the size of a bag of words that we will create as the last step and decrease the efficiency of an algorithm.
- Stemming: Take roots of the word
- Convert each word into its lower case: For example, it is useless to have some words in different cases (eg 'good' and 'GOOD').
Python
# 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 is going to be many columns.
- Rows are reviews
- If a word is there in the row of a dataset of reviews, then the count of the word will be there in the row of a 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 service"
Output :
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 the review is positive or negative that answer is in the second column of the dataset[:, 1]: all rows and 1st column (indexing from zero).
Python
# 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 separated 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).
Python
# Splitting the dataset into
# the Training set and Test set
from sklearn.model_selection 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)
# This code is modified by Susobhan Akhuli
Step 6: Fitting a Predictive Model (here random forest)
- Since Random forest is an ensemble model (made of many trees) from sklearn.ensemble, import RandomForestClassifier class
- With 501 trees or "n_estimators" and criterion as 'entropy'
- Fit the model via .fit() method with attributes X_train and y_train
Python
# 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: Predicting Final Results via using .predict() method with attribute X_test
Python
# Predicting the Test set results
y_pred = model.predict(X_test)
y_pred
Output :
array([0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0,
1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,
1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,
1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1,
1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 1, 0, 0, 1, 1])
Note: Accuracy with the random forest was 72%.(It may be different when performed an experiment with different test sizes, here = 0.25).
Step 8: To know the accuracy, a 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
Python
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
Output :
array([[102, 28],
[ 41, 79]])
Get the complete notebook and dataset link here:
Notebook link : click here.
Dataset link : click here
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Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Facebook Sentiment Analysis using pythonThis article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
6 min read
Next Sentence Prediction using BERTNext Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
4 min read
Hate Speech Detection using Deep LearningThere must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
5 min read
Image Caption Generator using Deep Learning on Flickr8K datasetGenerating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
12 min read
Movie recommendation based on emotion in PythonMovies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
4 min read
Speech Recognition in Python using Google Speech APISpeech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
2 min read
Voice Assistant using pythonSpeech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
3 min read
Human Activity Recognition - Using Deep Learning ModelHuman activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
6 min read
Fine-tuning BERT model for Sentiment AnalysisGoogle created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
6 min read
Sentiment Classification Using BERTBERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
12 min read
Sentiment Analysis with an Recurrent Neural Networks (RNN)Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
5 min read
Building an Autocorrector Using NLP in PythonAutocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
4 min read
Python | NLP analysis of Restaurant reviewsNatural 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 mach
7 min read
Restaurant Review Analysis Using NLP and SQLiteNormally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
9 min read
Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Clustering Projects
Recommender System Project