Paper: Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews

ACL ID P11-1150
Title Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

Inthispaper, wededicatetothetopicofaspect ranking, which aims to automatically identify important product aspects from online con- sumer reviews. The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and (b) consumers’ opinions on the important aspects greatly influence their overall opin- ions on the product. In particular, given con- sumer reviews of a product, we first identify the product aspects by a shallow dependency parser and determine consumers’ opinions on these aspects via a sentiment classifier. We then develop an aspect ranking algorithm to identify the important aspects by simultane- ously considering the aspect frequency and the influence of consumers’ opinions g...