IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection

Yue Chen; Yingnan Ju; Sandra Kübler

doi:10.18653/v1/2022.wassa-1.21

IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection

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Abstract

Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive.

Anthology ID:: 2022.wassa-1.21
Volume:: Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:: May
Year:: 2022
Address:: Dublin, Ireland
Editors:: Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani, João Sedoc, Roman Klinger, Alexandra Balahur
Venue:: WASSA
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 228–232
Language:
URL:: https://aclanthology.org/2022.wassa-1.21/
DOI:: 10.18653/v1/2022.wassa-1.21
Bibkey:
Cite (ACL):: Yue Chen, Yingnan Ju, and Sandra Kübler. 2022. IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 228–232, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):: IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection (Chen et al., WASSA 2022)
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PDF:: https://aclanthology.org/2022.wassa-1.21.pdf
Video:: https://aclanthology.org/2022.wassa-1.21.mp4

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@inproceedings{chen-etal-2022-iucl,
    title = "{IUCL} at {WASSA} 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection",
    author = {Chen, Yue  and
      Ju, Yingnan  and
      K{\"u}bler, Sandra},
    editor = "Barnes, Jeremy  and
      De Clercq, Orph{\'e}e  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Alqahtani, Sawsan  and
      Sedoc, Jo{\~a}o  and
      Klinger, Roman  and
      Balahur, Alexandra",
    booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.wassa-1.21/",
    doi = "10.18653/v1/2022.wassa-1.21",
    pages = "228--232",
    abstract = "Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive."
}

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%0 Conference Proceedings
%T IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection
%A Chen, Yue
%A Ju, Yingnan
%A Kübler, Sandra
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F chen-etal-2022-iucl
%X Our system, IUCL, participated in the WASSA 2022 Shared Task on Empathy Detection and Emotion Classification. Our main goal in building this system is to investigate how the use of demographic attributes influences performance. Our (official) results show that our text-only systems perform very competitively, ranking first in the empathy detection task, reaching an average Pearson correlation of 0.54, and second in the emotion classification task, reaching a Macro-F of 0.572. Our systems that use both text and demographic data are less competitive.
%R 10.18653/v1/2022.wassa-1.21
%U https://aclanthology.org/2022.wassa-1.21/
%U https://doi.org/10.18653/v1/2022.wassa-1.21
%P 228-232

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Markdown (Informal)

[IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection](https://aclanthology.org/2022.wassa-1.21/) (Chen et al., WASSA 2022)

IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection (Chen et al., WASSA 2022)

ACL

Yue Chen, Yingnan Ju, and Sandra Kübler. 2022. IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 228–232, Dublin, Ireland. Association for Computational Linguistics.