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 id...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2010
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| Subjects: | |
| 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 |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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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