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

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.