Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across Domains Article Swipe
Yufu Chen
,
Yanghui Rao
,
Shurui Chen
,
Zhiqi Lei
,
Haoran Xie
,
Raymond Y.K. Lau
,
Jian Yin
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3571736
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3571736
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.
Related Topics
Concepts
Computer science
Sentiment analysis
Domain adaptation
Artificial intelligence
Scalability
Domain (mathematical analysis)
Machine learning
Matrix decomposition
Word (group theory)
Dependency (UML)
Natural language processing
Data mining
Mathematics
Eigenvalues and eigenvectors
Mathematical analysis
Physics
Geometry
Classifier (UML)
Quantum mechanics
Database
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3571736
- OA Status
- bronze
- Cited By
- 4
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309287897
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309287897Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1145/3571736Digital Object Identifier
- Title
-
Semi-Supervised Sentiment Classification and Emotion Distribution Learning Across DomainsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-18Full publication date if available
- Authors
-
Yufu Chen, Yanghui Rao, Shurui Chen, Zhiqi Lei, Haoran Xie, Raymond Y.K. Lau, Jian YinList of authors in order
- Landing page
-
https://doi.org/10.1145/3571736Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://scholars.cityu.edu.hk/en/publications/semi-supervised-sentiment-classification-and-emotion-distributionDirect OA link when available
- Concepts
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Computer science, Sentiment analysis, Domain adaptation, Artificial intelligence, Scalability, Domain (mathematical analysis), Machine learning, Matrix decomposition, Word (group theory), Dependency (UML), Natural language processing, Data mining, Mathematics, Eigenvalues and eigenvectors, Mathematical analysis, Physics, Geometry, Classifier (UML), Quantum mechanics, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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