Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
Singapore Management University Institutional Knowledge (InK) (Singapore Management University)Published 9 October 2010Open access
Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
Citations414
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TL;DR
This paper proposes a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words and shows that with a relatively small amount of training data, this model can effectively identify aspect and opinion words simultaneously.
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.
Keywords
Computer Science
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