A Study of Different Approaches to Aspect-based Opinion Mining

Abstract

In recent years, Opinion mining has been an active research area in Text mining and analysis, natural language processing. Opinion mining is the computational study of people's opinion expressed in written language or text towards entities and their aspects. With the growth of internet, social networking sites, blogs, discussion forums, e-commerce websites have gained a tremendous importance and have provided platform for people to express and share their opinion on entities and their aspects. As opinionated web content is increasing rapidly in the form of reviews, comments, blogs, status updates, tweets, etc. it is practically impossible for people or organization to analyze all opinions at a time to make good decisions. Hence, there is a need for effective automated system to evaluate opinions and generate accurate results. This paper describes opinion mining and focuses on the sub topic aspect-based opinion mining, tasks in aspect-based opinion mining, current state-of-the-art methods used for aspect-based opinion mining, advantages and disadvantages of these methods and latest research challenges in aspect-based opinion mining. Our experimental results based on some of the aspect extraction techniques, gives an idea of which aspect extraction techniques are efficient and yield accurate results in practical opinion mining applications.