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.
|Title of host publication||Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020|
|Editors||Jing He, Hemant Purohit, Guangyan Huang, Xiaoying Gao, Ke Deng|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - Dec 2020|
|Event||2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Virtual Conference, Virtual, Online|
Duration: 14 Dec 2020 → 17 Dec 2020
http://wi2020.vcrab.com.au/WI-IAT-2020-Program.pdf (Conference Program)
|Name||Proceedings - 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2020|
|Conference||2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology|
|Abbreviated title||WI-IAT 2020|
|Period||14/12/20 → 17/12/20|
Bibliographical noteFunding 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.
© 2020 IEEE.
- Emotion classification
- Emotion lexicon
- Sentiment analysis
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Best Research Paper Award at the 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT '20)
XIE, Haoran (Recipient), Dec 2020
Prize: Prize (CDCF)