TY - JOUR
T1 - A constrained optimization approach for cross-domain emotion distribution learning
AU - Qin, Xiaorui
AU - Chen, Yufu
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Wong, Man Leung
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/9/5
Y1 - 2021/9/5
N2 - 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.
AB - 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.
KW - Content-based constraint
KW - Domain adaptation
KW - Emotion distribution learning
KW - Non-negative Matrix Tri-Factorization
UR - http://www.scopus.com/inward/record.url?scp=85108420209&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2021.107160
DO - 10.1016/j.knosys.2021.107160
M3 - Article
AN - SCOPUS:85108420209
SN - 0950-7051
VL - 227
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 107160
ER -