Reviewing Classification Approaches in Sentiment Analysis

Abstract

The advancement of web technologies has changed the way people share and express their opinions. People enthusiastically shared their thoughts and opinions via online media such as forums, blogs and social networks. The overwhelmed of online opinionated data have gained much attention by researchers especially in the field of text mining and natural language processing (NLP) to study in depth about sentiment analysis. There are several methods in classifying sentiment, including lexicon-based approach and machine learning approach. Each approach has its own advantages and disadvantages. However, there are not many literatures deliberate on the comparison of both approaches. This paper presents an overview of classification approaches in sentiment analysis. Various advantages and limitations of the sentiment classification approaches based on several criteria such as domain, classification type and accuracy are also discussed in this paper.