关键词: 观点评价对象抽取, 产品评论, 自举, 上下文相关

Abstract: Mining opinion targets from massive amount of product reviews is a key problem for opinion mining. Nevertheless nowaday the results of opinion target extraction only offer a little useful information. This paper presents a bidirectional bootstrapping approach which is based on context-sensitive to extract product names and features. The approach uses initial seeds and POS templates to get candidate opinion targets, then uses context-sensitive approach to extract opinion targets from the sentences which contain candidate opinion targets, and perform boundary detection. At the same time, it extracts and evaluates contexts templates of opinion targets, and puts the good templates into a template set. It iterates the approach until no new opinion targets emerge. The experimental results show that context-sensitive approach improves the performance by 10% compared with context-free approaches in extracting opinion targets.

Key words: opinion target extraction, product review, bootstrapping, context-sensitive