Paper: Aspect Extraction with Automated Prior Knowledge Learning

ACL ID P14-1033
Title Aspect Extraction with Automated Prior Knowledge Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the is- sue, several knowledge-based models have been proposed to incorporate prior knowl- edge provided by the user to guide mod- eling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possi- ble to learn prior knowledge automatically from a large amount of review data avail- able on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowl- edge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowled...