NDLI: Domain-dependent/independent topic switching model for online reviews with numerical ratings

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Domain-dependent/independent topic switching model for online reviews with numerical ratings

Content Provider	ACM Digital Library
Author	Nakamura, Takuma Matsumoto, Takashi Ida, Yasutoshi
Abstract	We propose a domain-dependent/independent topic switching model based on Bayesian probabilistic modeling for modeling online product reviews that are accompanied with numerical ratings provided by users. In this model, each word is allocated to a domain-dependent topic or a domain-independent topic, and the distribution of topics in an online review is connected to an observed numerical rating via a linear regression model. Domain-dependent topics utilize domain information observed with a corpus, and domain-independent topics utilize the framework of Bayesian Nonparametrics, which can estimate the number of topics in posterior distributions. The posterior distribution is estimated via collapsed Gibbs sampling. Using real data, our proposed model had smaller mean square error and smaller average mean error with a small model size and achieved convergence in fewer iterations for a regression task involving online review ratings, outperforming a baseline model that did not consider domains. Moreover, the proposed model can also tell us whether the words are positive or negative in the form of continuous values. This feature allows us to extract domain-dependent and -independent sentiment words.
Starting Page	229
Ending Page	238
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505540
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis Online reviews with numerical ratings Domain-dependent/independent topic switching model Hierarchical dirichlet process
Content Type	Text
Resource Type	Article

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