A Joint Model for Topic-Sentiment Modeling from Text

Traditional topic models, like LDA and PLSA, have been efficiently extended to capture further aspects of text in addition to the latent topics (e.g., time evolution, sentiment etc.). In this paper, we discuss the issue of joint topic sentiment modeling. We propose a novel topic model for topic -specific sentiment modeling from text and we derive an inference algorithm based on the Gibbs sampling process. We also propose a method for automatically setting the model parameters. The experiments performed on two review datasets show that our model outperforms other state-of-the-art models, in particular for sentiment prediction at the topic level.