Leveraging Latent Sentiment Constraint in Probabilistic Matrix Factorization for Cross-domain Sentiment Classification

Sentiment analysis is concerned with classifying a subjective text into positive or negative according to the opinion expressed in it. The performance of traditional sentiment classification algorithms rely heavily on manually labeled training data. However, not every domain has the labeled data because the labeling work is time-consuming and expensive. In this paper, we propose a latent sentiment factorization (LSF) algorithm based on probabilistic matrix factorization technique for cross-domain sentiment classification. LSF works in the setting where there are only labeled data in the source domain and unlabeled data in the target domain. It bridges the gap between domains by exploiting the sentiment correlations between domain-shared and domain-specific words in a two-dimensional sentiment space. Experimental results demonstrate the superiority of our method over the state-of-the-art approaches.