Aspect based fine-grained sentiment analysis for online reviews

Abstract

Fine-grained sentiment analysis for online reviews plays more and more important role in many applications. The key techniques here are how to efficiently extract multi-grained aspects, identify associated opinions, and classify sentiment polarity. Although various topic models have been proposed to process some of these tasks in recent years, there was little work available for effective sentiment analysis. In this paper, we propose a joint aspect based sentiment topic (JABST) model that jointly extracts multi-grained aspects and opinions through modeling aspects, opinions, sentiment polarities and granularities simultaneously. Moreover, by means of the supervised learning, we then propose a maximum entropy based JABST model (MaxEnt–JABST) to improve accuracy and performance in extracting opinions and aspects. Comprehensive evaluation results on real-world reviews for electronic devices and restaurants demonstrate that our JABST and MaxEnt–JABST models significantly outperform related proposals.