Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid

نویسندگان

  • Wayne Xin Zhao
  • Jing Jiang
  • Hongfei Yan
  • Xiaoming Li
چکیده

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.

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تاریخ انتشار 2010