Siamese Network-Based Supervised Topic Modeling

Abstract

Label-specific topics can be widely used for supporting personality psychology, aspectlevel sentiment analysis, and cross-domain sentiment classification. To generate labelspecific topics, several supervised topic models which adopt likelihood-driven objective functions have been proposed. However, it is hard for them to get a precise estimation on both topic discovery and supervised learning. In this study, we propose a supervised topic model based on the Siamese network, which can trade off label-specific word distributions with document-specific label distributions in a uniform framework. Experiments on realworld datasets validate that our model performs competitive in topic discovery quantitatively and qualitatively. Furthermore, the proposed model can effectively predict categorical or real-valued labels for new documents by generating word embeddings from a labelspecific topical space.