SAE: Syntactic-based aspect and opinion extraction from product reviews

Abstract

Aspect extraction is an important task in sentiment analysis to identify aspects in customer review products. Most existing works defines the pattern set manually or using heuristic approach. In this paper, we propose SAE, a Syntactical-based Aspect Extraction using decision tree and rule learning to generate the pattern set based on sequence labelling. We provide a comprehensive analysis of aspect extraction using pattern-based method and typed-dependency. The patterns will be used to identify and extract aspect term candidates in customer product review. First, we generate pattern set that identify aspect term candidates using decision tree and rule learning such as ID3, J48, Random Tree, Part and Prism, based on sequence labelling. The set of pattern is employed to produced aspect term candidates. We use a list of positive and negative opinion lexicon as aspect term candidates filtering. Finally, we combine the pattern-based method with typed dependency to remove irrelevant aspect term. The results showed that the combination of pattern-based and typed dependency can increase the performance. However, since our work is based on syntactic-based approach, it can be used to other domains, that is expected to include an unlimited domain datasets.