EmoChannelAttn : Exploring emotional construction towards multi-class emotion classification

Zongxi LI, Xinhong CHEN, Haoran XIE, Qing LI, Xiaohui TAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020
EditorsJing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages242-249
Number of pages8
ISBN (Electronic)9781665419246
DOIs
Publication statusPublished - Dec 2020
Event2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Virtual Conference, Virtual, Online
Duration: 14 Dec 202017 Dec 2020
http://wi2020.vcrab.com.au/
http://wi2020.vcrab.com.au/WI-IAT-2020-Program.pdf (Conference Program)

Publication series

NameProceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020

Conference

Conference2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology
Abbreviated titleWI-IAT 2020
CityVirtual, Online
Period14/12/2017/12/20
Internet address

Bibliographical note

Funding Information:
The research described in this article has been supported by the HKIBS Research Seed Fund 2019/20 (190-009) and the Research Seed Fund (102367) of Lingnan University, Hong Kong.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Emochannel
  • Emotion classification
  • Emotion lexicon
  • Sentiment analysis

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