Sentiment Analysis in Online Social Networks

Abstract

The convenience of being able to shop from home has led to the rise of e-commerce in today's highly digitized society. Before buying anything online, customers are required to read hundreds of reviews. Tracking and analyzing customer feedback may be challenging when there are millions of online reviews for a single product. However, in today’s age of machine learning, if a model were used to polarize and comprehend from them, thousands of input and information might be gained quickly and easily. As a result, sentiment analysis has emerged as a distinct field of research that integrates NLP and text analytics to identify and categorize the emotional tone of written content. In this dissertation, we investigate the difficulty of labelling reviews as positive, negative, or neutral. For massive amounts of supervised data, like those seen in the Amazon dataset, we have found success using KNN, Logistic regression, and Random Forest Classifiers. Meanwhile, the greatest results were obtained using the Logistic and random forest classifiers, and we plan to develop a web application using these models to categorize the reviews in real time. Finally, this research delves into sentiment analysis and opinion mining with regards to product feedback.