Paper: An Unsupervised Aspect-Sentiment Model for Online Reviews

ACL ID N10-1122
Title An Unsupervised Aspect-Sentiment Model for Online Reviews
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

With the increase in popularity of online re- view sites comes a corresponding need for tools capable of extracting the information most important to the user from the plain text data. Due to the diversity in products and ser- vices being reviewed, supervised methods are often not practical. We present an unsuper- vised system for extracting aspects and deter- mining sentiment in review text. The method is simple and flexible with regard to domain and language, and takes into account the in- fluence of aspect on sentiment polarity, an is- sue largely ignored in previous literature. We demonstrate its effectiveness on both compo- nent tasks, where it achieves similar results to more complex semi-supervised methods that are restricted by their reliance on manual an- notation and extensive kn...