An Unsupervised Aspect-Sentiment Model for Online Reviews

With the increase in popularity of online review 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 services being reviewed, supervised methods are often not practical. We present an unsupervised system for extracting aspects and determining sentiment in review text. The method is simple and flexible with regard to domain and language, and takes into account the influence of aspect on sentiment polarity, an issue largely ignored in previous literature. We demonstrate its effectiveness on both component tasks, where it achieves similar results to more complex semi-supervised methods that are restricted by their reliance on manual annotation and extensive knowledge sources.

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Samuel Brody, Noemie Elhadad
• Keywords: Computational Linguistics, Extensive Knowledge Sources, NAACL 2010, Online Review Sites, Plain Text Data