Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid

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.

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Wayne Xin Zhao, Jing Jiang, Hongfei Yan, Xiaoming Li
• Keywords: Aspect-specific Opinion Words, EMNLP 2010, Natural Language Processing, Opinion Words, Topic Models