TY - GEN
T1 - Topic model for user reviews with adaptive windows
AU - Konishi, Takuya
AU - Kimura, Fuminori
AU - Maeda, Akira
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84875437522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875437522&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-36973-5_71
DO - 10.1007/978-3-642-36973-5_71
M3 - Conference contribution
AN - SCOPUS:84875437522
SN - 9783642369728
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 730
EP - 733
BT - Advances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings
T2 - 35th European Conference on Information Retrieval, ECIR 2013
Y2 - 24 March 2013 through 27 March 2013
ER -