Paper: Aspect Extraction through Semi-Supervised Modeling

ACL ID P12-1036
Title Aspect Extraction through Semi-Supervised Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised topic modeling. By categorizing, we mean the synonymous aspects should be clustered into the same category. In this paper, we solve the problem in a different setting where the user provides some seed words for a few aspect categories and the model extracts and clusters aspect terms into categories simultaneously. This setting is important because categorizing aspects is a subjective task. For different application purposes, different categorizations may be needed. Some form of user guidance is desired. In this paper, we propose two statistical models to solve this seeded problem, which aim to di...