Enhancing Sentiment Analysis using Machine Learning Predictive Models to Analyze Social Media Reviews on Junk Food

Abstract

In the last few years, the Use of social media has increased immensely. People share different types of opinions on social media like Facebook posts, comments, tweets etc. Sentiment analysis involves the process of categorizing these opinions. The aim of this study, find out the customer’s attitudes toward the restaurant. Nowadays sentiment review is gaining grip. The benefits of this sentiment analysis for restaurants is how customers like their food and as a result, the business of Bangladeshi restaurants will be more developed. The study focuses primarily on customers’ behavior, tastes, preferences, conversations, reviews, and objections. For this purpose 500 data are collected. There are six attributes in the dataset and based on customer reviews they are satisfied or unsatisfied. This exploration uses different classifiers of ML to develop review analysis like SVM, Random Forest, K-nearest neighbors, Decision Tree, Logistic Regression and XGBoost Classifier. And Comparing these algorithms’ performances, XGBOOST gives the greatest accuracy which is 83%.