Sentiment Analysis on Amazon Customer's Review Using NLP

Abstract

The business environment of today has grown incredibly competitive and difficult. Growth of businesses now places a lot of emphasis on customer happiness. To understand and meet the demands of their clients, business organizations devote a significant amount of money and human resources to different techniques. But many businesses are failing to satisfy customers as a result of the manual analysis of customers varied wants being done in an imperfect way. As a result, they are losing their customers' trust and increasing their marketing expenses. Sentiment Analysis is a solution that we can use to resolve the issues. Machine learning (ML) and natural language processing are both included into the system (NLP). Analysis of people's feelings about certain topics, products, and services is called sentiment analysis, and it is used rather often to get insights into how the general public thinks about certain topics, products, and services. We are able to do that by using any data that is found online. In this article, we present two natural language processing strategies (Bag-of-Words and TF-IDF) as well as various machine learning classification techniques to perform sentiment analysis on a large dataset that is imbalanced and contains many classes of data.