EmoChannel-SA: Exploring emotional dependency towards classification task with self-attention mechanism

Zongxi LI, Xinhong CHEN, Haoran XIE, Qing LI, Xiaohui TAO, Kwok Shing CHENG

Research output: Contribution to journalArticlespeer-review

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. Copyright © 2021 The Author(s).
Original languageEnglish
JournalWorld Wide Web
Early online date06 Oct 2021
DOIs
Publication statusE-pub ahead of print - 06 Oct 2021

Citation

Li, Z., Chen, X., Xie, H., Li, Q., Tao, X., & Cheng, G. (2021). EmoChannel-SA: Exploring emotional dependency towards classification task with self-attention mechanism. World Wide Web. Advance online publication. doi: 10.1007/s11280-021-00957-5

Keywords

  • Sentiment analysis
  • Emotion classification
  • Emotion lexicon
  • Emochannel

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