A constrained optimization approach for cross-domain emotion distribution learning

Xiaorui Qin, Yufu Chen, Yanghui Rao, Haoran Xie, Man Leung Wong, Fu Lee Wang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Emotion distribution learning aims to annotate unlabeled instances with a set of emotion categories and their strengths. Non-negative Matrix Tri-Factorization (NMTF) introduces an association matrix between document clusters and word clusters to help the domain adaptation task in emotion distribution learning. Nevertheless, many prior cross-domain emotion distribution learning methods had two major deficiencies. First, they hypothesize that there is a one-to-one correspondence between document clusters and emotion labels. In their experiments, the number of document clusters depends on the number of labels. Second, the prior work does not endow models with adequate constraints. In the real scenario of cross-domain emotion distribution learning, there are potential constraints that may improve the performance of such models. In order to address these problems, we propose a constrained optimization approach based on NMTF for cross-domain emotion distribution learning. In our model, the relationship between document clusters and emotion labels is not always one-to-one. A novel content-based constraint is also endowed based on the hypothesis that documents belonging to the same clusters must have similar content. We solve the optimization problem by an alternately iterative algorithm and show the proof of convergence. Experiments on 12 real-world cross-domain emotion distribution learning tasks validate the effectiveness of our method.

Original languageEnglish
Article number107160
JournalKnowledge-Based Systems
Publication statusPublished - 5 Sept 2021


  • Content-based constraint
  • Domain adaptation
  • Emotion distribution learning
  • Non-negative Matrix Tri-Factorization


Dive into the research topics of 'A constrained optimization approach for cross-domain emotion distribution learning'. Together they form a unique fingerprint.

Cite this