Semi-supervised Sentiment Classification and Emotion Distribution Learning Across Domains

Yufu CHEN, Yanghui RAO*, Shurui CHEN, Zhiqi LEI, Haoran XIE, Raymond Y. K. LAU, Jian YIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

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 for model training. By introducing a shared matrix 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.
Original languageEnglish
Article number74
Number of pages30
JournalACM Transactions on Knowledge Discovery from Data
Volume17
Issue number5
Early online date18 Nov 2022
DOIs
Publication statusPublished - Jun 2023

Bibliographical note

Funding Information:
The research described in this article was supported by the National Natural Science Foundation of China (61972426). The work of Haoran Xie was supported by Lam Woo Research Fund (LWP20019), and the Faculty Research Grants (DB22B4 and DB22B7) of Lingnan University, Hong Kong. The work of Raymond Y. K. Lau was supported by a grant from the Research Grants Council of the HKSAR, China (Project: CityU 11507219), and a grant from the City University of Hong Kong SRG (Project: 7005780). The work of Jian Yin was supported by the National Natural Science Foundation of China under Grants U1811264, U1811262, U1811261, U1911203, U2001211, and U22B2060.

Publisher Copyright:
© 2023 Association for Computing Machinery.

Keywords

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

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