Topic models with sentiment priors based on distributed representations

Abstract

There are provided a device and method for topic modeling with sentiment priors based on distributed representations. A method for topic modeling with sentiment priors based on distributed representations includes: inputting a review into a topic model; and by the topic model, determining a representation for each word in the review, wherein the representations are word vectors in a semantic space; and estimating the representations using the sentiment priors to determine a topic corresponding to the review, wherein the topic model includes the sentiment priors trained on the basis of the representations distributed by using a regularizer, the regularizer defining the same sentiment to words having similar word vectors, and wherein each sentiment prior is the same for words having similar word vectors.