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

Li, Zongxi and Chen, Xinhong and Xie, Haoran and Li, Qing and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Cheng, Gary (2021) EmoChannel-SA: exploring emotional dependency towards classification task with self-attention mechanism. World Wide Web, 24 (6). pp. 2049-2070. ISSN 1386-145X

[img]
Preview
Text (Published Version)
EmoChannel-SA exploring emotional dependency towards classification task with self-attention mechanism.pdf
Available under License Creative Commons Attribution 4.0.

Download (2MB) | Preview

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.


Statistics for USQ ePrint 47313
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 02 Mar 2022 02:02
Last Modified: 02 Mar 2022 02:02
Uncontrolled Keywords: Sentiment analysis; Emotion classification; Emotion lexicon; Emochannel
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460507 Information extraction and fusion
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Identification Number or DOI: https://doi.org/10.1007/s11280-021-00957-5
URI: http://eprints.usq.edu.au/id/eprint/47313

Actions (login required)

View Item Archive Repository Staff Only