Description

Co-Extracting opinion targets and opinion words from online reviews are two fundamental tasks in opinion mining. Opinion mining or sentiment analysis is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. This paper presents an alignment-based approach with graph co-ranking to collectively extract opinion targets and opinion words. The system propose a method based on a monolingual word alignment model (WAM). Compared to previous nearest-neighbor rules, the WAM does not constrain identifying modified relations to a limited window; therefore, it can capture more complex relations, such as long-span modified relations. This paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then this paper proposes a novel approach to collectively extract them with graph co-ranking.