A knowledge-rich approach to feature-based opinion extraction from product reviews

Abstract

Feature-based opinion extraction is a task related to information extraction, which consists of extracting structured opinions on features of some object from reviews or other subjective textual sources. Over the last years, this problem has been studied by some researchers, generally in an unsupervised, domain-independent manner. As opposed to that, in this work we propose a redefinition of the problem from a more practical point of view, and describe a domain-specific, resource-based opinion extraction system. We focus on the description and generation of those resources, and briefly report the extraction system architecture and a few initial experiments. The results suggest that domain-specific knowledge is a valuable resource in order to build precise opinion extraction systems.