Paper Info
Title
EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism
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.
Year
DOI
Venue
2021
10.1007/s11280-021-00957-5
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Keywords
DocType
Volume
Sentiment analysis, Emotion classification, Emotion lexicon, Emochannel
Journal
24
Issue
ISSN
Citations 
6
1386-145X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Zongxi Li100.34
Xinhong Chen200.34
Haoran Xie301.01
Qing Li401.01
Xiaohui Tao501.35
Gary Cheng632.73