An Effectual Opinion Mining of Sentiment in Bistro Data using Machine Learning Schemes

Abstract

            Sentiment analysis is creating an immense area of research in this modern epoch of social media. Different blogs and Social Medias (twitter, Instagram, Facebook, etc.,) are the most trendy platform for the users or customers where they regularly express their opinion about current topics, diverse brands, movies, books, restaurants, traveling places, etc. Sentiment analysis is a very elegant and effectual approach to find peoples vision about a particular place/news/restaurant/movie/brand/book. It is useful for the service providers, sellers and customers. Researchers in the areas of NLP, machine learning, data mining, and others have tested different schemes of automating the process of sentiment analysis. In this paper, we used restaurant reviews dataset in analyzing the sentiment and Gaussian Naïve Bayes scheme is articulated based on coupling classification methods by means of arcing classifier and their performances are analyzed in terms of accuracy.