Aspect Extraction through Semi-Supervised Modeling

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 discover exactly what the user wants. Our experimental results show that the two proposed models are indeed able to perform the task effectively.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Arjun Mukherjee, Bing Liu 0001
• Keywords: ACL 2012, Computational Linguistics, Extracts, Sentiment Analysis, Statistical Models