TY - GEN
T1 - Building Chinese affective resources in valence-arousal dimensions
AU - Yu, Liang Chih
AU - Lee, Lung Hao
AU - Hao, Shuai
AU - Wang, Jin
AU - He, Yunchao
AU - Hu, Jun
AU - Lai, K. Robert
AU - Zhang, Xuejie
N1 - Publisher Copyright:
©2016 Association for Computational Linguistics.
PY - 2016
Y1 - 2016
N2 - An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple dimensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several classes (e.g., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. However, affective resources with valence-arousal ratings are still very rare, especially for the Chinese language. Therefore, this study builds 1) an affective lexicon called Chinese valence-arousal words (CVAW) containing 1,653 words, and 2) an affective corpus called Chinese valence-arousal text (CVAT) containing 2,009 sentences extracted from web texts. To improve the annotation quality, a corpus cleanup procedure is used to remove outlier ratings and improper texts. Experiments using CVAW words to predict the VA ratings of the CVAT corpus show results comparable to those obtained using English affective resources.
AB - An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple dimensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several classes (e.g., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. However, affective resources with valence-arousal ratings are still very rare, especially for the Chinese language. Therefore, this study builds 1) an affective lexicon called Chinese valence-arousal words (CVAW) containing 1,653 words, and 2) an affective corpus called Chinese valence-arousal text (CVAT) containing 2,009 sentences extracted from web texts. To improve the annotation quality, a corpus cleanup procedure is used to remove outlier ratings and improper texts. Experiments using CVAW words to predict the VA ratings of the CVAT corpus show results comparable to those obtained using English affective resources.
UR - http://www.scopus.com/inward/record.url?scp=84994140361&partnerID=8YFLogxK
U2 - 10.18653/v1/n16-1066
DO - 10.18653/v1/n16-1066
M3 - 會議論文篇章
AN - SCOPUS:84994140361
T3 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
SP - 540
EP - 545
BT - 2016 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Y2 - 12 June 2016 through 17 June 2016
ER -