Techniques and Approaches for Sentiment Analysis in Social Media

Abstract

The sentiment of a person (opinion) can be expressed through speech or writing in a specific natural language. Sentiment analysis (SA) often aims to identify the opinions of a writer or speaker on a particular topic or the general contextual polarity of a document. Sentiment analysis is widely employed in social media and reviews for a variety of purposes, such as customer service, political reviews, policymaking, marketing research, and decision-making. Machine learning (ML) approaches allow for the extraction of inferences from user interactions. Emotions are analyzed using a variety of machine learning approaches, such as deep learning (DL), supervised, semi-supervised, and unsupervised learning. In this chapter, various methodologies for sentiment classification are introduced in this most challenging area of sentiment analysis. This study gives academics a worldwide perspective on the analysis of feelings and its related domain, applications, and obstacles by providing an in-depth discussion of sentiment analysis methodologies.