JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis

Abstract

Cross-domain sentiment classification (CSC) aims at learning a sentiment classifier for unlabeled data in the target domain based on the labeled data from a different source domain. Due to the differences of data distribution of two domains in terms of the raw features, the CSC problem is difficult and challenging. Previous researches mainly focused on concepts mining by clustering words across data domains, which ignored the importance of authors' emotion contained in data, or the different representations of the emotion between domains. In this paper, we propose a novel framework to solve the CSC problem, by modelling the emotion across domains. We first develop a probabilistic model named JEAM to model author's emotion state when writing. Then, an EM algorithm is introduced to solve the likelihood maximum problem and to obtain the latent emotion distribution of the author. Finally, a supervised learning method is utilized to assign the sentiment polarity to a given online review. Experiments show that our approach is effective and outperforms state-of-the-art approaches.