Projects per year
Abstract
Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence’s emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets.
Original language | English |
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Article number | 6 |
Pages (from-to) | 2049-2070 |
Number of pages | 22 |
Journal | World Wide Web |
Volume | 24 |
Issue number | 6 |
Early online date | 6 Oct 2021 |
DOIs | |
Publication status | Published - Nov 2021 |
Bibliographical note
Publisher Copyright:© 2021, The Author(s).
Keywords
- Emochannel
- Emotion classification
- Emotion lexicon
- Sentiment analysis
Fingerprint
Dive into the research topics of 'EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism'. Together they form a unique fingerprint.Projects
- 2 Finished
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Facilitate Tree-Structured Topic Modeling via Nonparametric Neural Inference
XIE, H. (PI)
1/03/21 → 28/02/22
Project: Grant Research
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Preliminary Study on Artificial Intelligence Techniques for Learning Emotions from Short Text
XIE, H. (PI) & LAM, W. (CoI)
1/09/20 → 31/08/21
Project: Grant Research
Research output
- 5 Scopus Citations
- 1 Conference paper (refereed)
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EmoChannelAttn : Exploring emotional construction towards multi-class emotion classification
LI, Z., CHEN, X., XIE, H., LI, Q. & TAO, X., Dec 2020, Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020. He, J., Purohit, H., Huang, G., Gao, X. & Deng, K. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 242-249 8 p. 9457707. (Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020).Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review
2 Citations (Scopus)