Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid

• Main Authors: ZHAO, Xin, JIANG, Jing, YAN, Hongfei, LI, Xiaoming
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/645; https://ink.library.smu.edu.sg/context/sis_research/article/1644/viewcontent/Jointly_Modeling_Aspects_and_Opinions_Jing_2010.pdf
• Institution: Singapore Management University

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.