Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2010
Abstract
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.
Discipline
Databases and Information Systems
Publication
Procceedings of the Conference on Empirical Methods in Natural Language Processing: 9-11 October 2010, MIT, Massachusetts, USA
First Page
56
Last Page
65
Publisher
ACL
City or Country
Boston, MA
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/645
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.aclweb.org/anthology/D/D10/D10-1006.pdf