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        <title>ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts</title>
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        <namePart type="given">Liang-Chih</namePart>
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    <abstract>This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 7 teams registered for this shared task for two-dimensional sentiment analysis, 6 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques for the educational domain. All data sets with gold standards and scoring script are made publicly available to researchers.</abstract>
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%0 Conference Proceedings
%T ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts
%A Yu, Liang-Chih
%A Wang, Jin
%A Peng, Bo
%A Huang, Chu-Ren
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F yu-etal-2021-rocling
%X This paper presents the ROCLING 2021 shared task on dimensional sentiment analysis for educational texts which seeks to identify a real-value sentiment score of self-evaluation comments written by Chinese students in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 7 teams registered for this shared task for two-dimensional sentiment analysis, 6 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques for the educational domain. All data sets with gold standards and scoring script are made publicly available to researchers.
%U https://aclanthology.org/2021.rocling-1.51
%P 385-388

Markdown (Informal)

[ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts](https://aclanthology.org/2021.rocling-1.51) (Yu et al., ROCLING 2021)

• ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts (Yu et al., ROCLING 2021)

ACL

• Liang-Chih Yu, Jin Wang, Bo Peng, and Chu-Ren Huang. 2021. ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 385–388, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).