@inproceedings{24375798c2844dcba9ed96b47a34d800,
title = "ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts",
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.",
author = "Yu, {Liang Chih} and Jin Wang and Bo Peng and Huang, {Chu Ren}",
note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",
year = "2021",
month = oct,
language = "English",
series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "385--388",
editor = "Lung-Hao Lee and Chia-Hui Chang and Kuan-Yu Chen",
booktitle = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
}