A comparative study of target-based and entity-based opinion extraction

Abstract

Opinion target extraction is a crucial task of opinion mining, aiming to extract occurrences of the different entities of a corpus that are subjects of an opinion. In order to produce a readable and comprehensible opinion summary, which is the main application of opinion target extraction, these occurrences are consolidated at the entity level in a second task. In this paper we argue that combining the two tasks, i.e. extracting opinion targets using entities as labels instead of binary labels, yields better results for opinion target extraction. We compare the binary approach and the multi-class approach on available datasets in English and French, and conduct several investigation experiments to explain the promising results. Our experiment show that an entity-based labelling not only improves opinion extraction in a single domain setting, but also let us combine training data from different domains to improve the extraction, a result that has never been achieved on target-based training data.