EmoChannelAttn: Exploring Emotional Construction Towards Multi-Class Emotion Classification

Li, Zongxi and Chen, Xinhong and Xie, Haoran and Li, Qing and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X (2021) EmoChannelAttn: Exploring Emotional Construction Towards Multi-Class Emotion Classification. In: 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2020), 14 Dec - 17 Dec 2020, Melbourne, Australia.


Abstract

The current multi-class emotion classification studies mainly focus on enhancing word-level and sentence-level semantical and sentimental features by exploiting hand-crafted lexicon dictionaries. In comparison, very limited studies attempt to achieve emotion classification task from the emotion-level perspectives, which are to understand how the emotion of a sentence is constructed. Another limitation of existing works is that they assumed that emotion labels are relatively independent, neglecting the possible relations among different types of emotions. Therefore, in this work, we aim to explore various fine-grained emotions based on domain knowledge to understand the construction details of emotions and the interconnection among emotions. To address the first issue, we propose a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series by incorporating domain knowledge and dimensional sentiment lexicons. The resulting information of 151 available fine-grained emotions is utilized to comprise the sentence-level emotion construction. As for the second issue, we introduce the EmoChannelAttn Network to identify the dependency relationship within all emotions via attention mechanism to enhance emotion classification performance. Our experiments demonstrate that the proposed method gains significant improvements compared with baseline models on several multi-class datasets.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
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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: 11 May 2022 04:36
Last Modified: 31 May 2022 00:07
Uncontrolled Keywords: emotion classification, sentiment analysis, emotion lexicon, emochannel
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Fields of Research (2020): 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
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
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/10.1109/WIIAT50758.2020.00036
URI: http://eprints.usq.edu.au/id/eprint/46213

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