Sentimental Analysis of Online Restaurant Reviews

Abstract

Sentimental Analysis is a Natural Language Processing (NLP) procedure used to characterize records for recognizing positive or negative reviews. Customer happiness has recently emerged as one of the most important criteria in the restaurant industry's success. The importance of consumer feedback cannot be overstated. For the sake of social media, people are more motivated to read reviews before coming to a restaurant. Customers who want to choose a restaurant may read a lot of reviews to get a good idea of the restaurant's quality or services. As a result, a nostalgic classification of a large number of audits is required to achieve meaningful experiences, allowing customers to select a restaurant based on their preferences. Sentimental analysis can help with this classification. This research suggests a system for categorizing customer reviews into good and negative categories based on sentimental input. 1000 restaurant evaluations from Tripadvisor, foodpanda, foodbank, and other restaurant review sites were used to test the proposed solutions. In this paper Split Test, 20% Data Testing and 80% Data Training have been used. More specifically, the proposed system has been tested with four supervised algorithms of machine learning: Support Vector Machine (SVM), Multinomial Naïve Bayes, Random Forest and Decision Tree for sentiment classification of comments. The untried result shows that this proposed system can classify restaurant reviews with 71.50% accuracy using SVM, 73% accuracy using Multinomial Naive Bayes, 70.50% accuracy using Random Forest, 65%using Decision Trees.