Conference Proceeding Article
Discovering and summarizing opinions from online reviews is an important and challenging task. A commonly-adopted framework generates structured review summaries with aspects and opinions. Recently topic models have been used to identify meaningful review aspects, but existing topic models do not identify aspect-specific opinion words. In this paper, we propose a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words. We show that with a relatively small amount of training data, our model can effectively identify aspect and opinion words simultaneously. We also demonstrate the domain adaptability of our model.
Procceedings of the Conference on Empirical Methods in Natural Language Processing: 9-11 October 2010, MIT, Massachusetts, USA
City or Country
ZHAO, Xin; JIANG, Jing; YAN, Hongfei; and LI, Xiaoming.
Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid. (2010). Procceedings of the Conference on Empirical Methods in Natural Language Processing: 9-11 October 2010, MIT, Massachusetts, USA. 56-65. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/645