Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across Domains

Authors：Chen, Yufu; Rao, Yanghui^*; Chen, Shurui; Lei, Zhiqi; Xie, Haoran; Lau, Raymond Y. K.; Yin, Jian

Source：ACM Transactions on Knowledge Discovery from Data, 2023, 17(5): 74.

DOI：10.1145/3571736

• Semi-supervised learning
• sentiment classification
• emotion distribution
• learning
• non-negative matrix tri-factorization
• label dependency

In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain formodel training. By introducing a sharedmatrix that captures the stable association between document clusters and word clusters, non-negative matrix tri-factorization (NMTF) is robust to the labeled target domain data and has shown remarkable performance in cross-domain text classification. However, the existing NMTF-based models ignore the incompatible relationship of sentiment polarities and the relatedness among emotions. Besides, their applications on large-scale datasets are limited by the high computation complexity. To address these issues, we propose a semi-supervised NMTF framework for sentiment classification and emotion distribution learning across domains. Based on a many-to-many mapping between document clusters and sentiment polarities (or emotions), we first incorporate the prior information of label dependency to improve the model performance. Then, we develop a parallel algorithm based on message passing interface (MPI) to further enhance the model scalability. Extensive experiments on real-world datasets validate the effectiveness of our method.

• Institution
  中山大学