Machine Learning-Based Approach for Opinion Mining and Sentiment Polarity Estimation

Abstract

Opinions of others may be essential in making decisions or selecting from a variety of alternatives. Review of customer feedback helps to improve sales and eventually benefits the company. Most online businesses use recommendation systems which use data mining and machine learning algorithms to find the right product for the right customer at the right time to increase customer satisfaction. This study illustrates how to increase the quality of the product selection process for customers by reducing information overloading and complexity. The goal of this study is to propose a novel product ranking model considering user reviews which enable multiuser recommendation. Dataset was taken through some different supervised learning methods and the best accurate algorithm was proposed. Values are predicted considering positivity and negativity of reviews for a particular product using the proposed algorithm. Products are ranked according to the given value. New recommendation model and its workflow are illustrated here.