Topic model for user reviews with adaptive windows

Authors:
Takuya Konishi;Fuminori Kimura;Akira Maeda
Affiliations:
Nara Institute of Science and Technology, Ikoma, Nara, Japan;Ritsumeikan University, Kusatsu, Shiga, Japan;Ritsumeikan University, Kusatsu, Shiga, Japan
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

Citing 3
Cited 0

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Modeling online reviews with multi-grain topic models

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Distance Dependent Chinese Restaurant Processes

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Abstract

We discuss the problem in applying topic models to user reviews. Different from ordinary documents, reviews in a same category are similar to each other. This makes it difficult to estimate meaningful topics from these reviews. In this paper, we develop a new model for this problem using the distance dependent Chinese restaurant process. It need not decide the size of windows and can consider neighboring sentences adaptively. We compare this model to the Multi-grain latent Dirichlet allocation which has been proposed previously, and show that our model achieves better results in terms of perplexity.