Topic model for user reviews with adaptive windows

Takuya Konishi, Fuminori Kimura, Akira Maeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings
Number of pages4
Publication statusPublished - 2013
Externally publishedYes
Event35th European Conference on Information Retrieval, ECIR 2013 - Moscow, Russian Federation
Duration: Mar 24 2013Mar 27 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7814 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th European Conference on Information Retrieval, ECIR 2013
Country/TerritoryRussian Federation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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