A Topic Modeling-Guided Framework for Aspect-Oriented Sentiment Analysis on Social Media

Abstract

Social media platforms have incorporated more than half of the world's population, making it one of the most data-rich domains recently. The sentiments expressed by social media users hold great significance for various reasons, such as the identification of public opinion on a product or towards a governmental policy, to name a few. There are different domains where companies use social media sentiments to gather feedback from customers to provide them with better products and services. Only a few attempts have been reported on aspect-based sentiment analysis literature on sentiment analysis and opinion mining. This chapter proposes a framework for aspect-based sentiment analysis for social media using a topic modeling-powered approach. The experiments conducted on real-world datasets show that the proposed framework outperforms some existing works on aspect-oriented sentiment analysis.