IASL valence-Arousal analysis system at IALP 2016 shared task: Dimensional sentiment analysis for Chinese words

Yu Lun Hsieh, Chen Ann Wang, Ying Wei Wu, Yung Chun Chang, Wen Lian Hsu

研究成果: 書貢獻/報告類型 › 會議貢獻

1 引文 (Scopus)

摘要

Sentiment lexicons with valence-Arousal ratings are useful resources for the development of dimensional sentiment applications. In order to solve the significant lack of Chinese valence and arousal lexicons, the objective of the DSAW is to automatically acquire the valence-Arousal ratings of Chinese affective words. In this task, we develop a novel approach that integrate word embeddings into a graph-based model with K-Nearest Neighbor to identify both valence and arousal dimensions. We also propose to use character embeddings to represent unseen words, which is a major challenge in collecting large corpora. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese words with 0.847 and 1.281 mean absolute error (MAE) for valence and arousal respectively.
原文英語
主出版物標題Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面297-299
頁數3
ISBN（電子）9781509009213
DOIs
出版狀態已發佈 - 三月 10 2017
對外發佈Yes
事件20th International Conference on Asian Language Processing, IALP 2016 - Tainan, 臺灣
持續時間: 十一月 21 2016 → 十一月 23 2016

會議

會議20th International Conference on Asian Language Processing, IALP 2016
國家臺灣
城市Tainan
期間11/21/16 → 11/23/16

指紋

dimensional analysis
systems analysis
rating
lack
evaluation
resources

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Linguistics and Language
  • Artificial Intelligence

引用此文

Hsieh, Y. L., Wang, C. A., Wu, Y. W., Chang, Y. C., & Hsu, W. L. (2017). IASL valence-Arousal analysis system at IALP 2016 shared task: Dimensional sentiment analysis for Chinese words. 於 Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016 (頁 297-299). [7875990] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IALP.2016.7875990

IASL valence-Arousal analysis system at IALP 2016 shared task : Dimensional sentiment analysis for Chinese words. / Hsieh, Yu Lun; Wang, Chen Ann; Wu, Ying Wei; Chang, Yung Chun; Hsu, Wen Lian.

Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 297-299 7875990.

研究成果: 書貢獻/報告類型 › 會議貢獻

Hsieh, YL, Wang, CA, Wu, YW, Chang, YC & Hsu, WL 2017, IASL valence-Arousal analysis system at IALP 2016 shared task: Dimensional sentiment analysis for Chinese words. 於 Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016., 7875990, Institute of Electrical and Electronics Engineers Inc., 頁 297-299, 20th International Conference on Asian Language Processing, IALP 2016, Tainan, 臺灣, 11/21/16. https://doi.org/10.1109/IALP.2016.7875990
Hsieh YL, Wang CA, Wu YW, Chang YC, Hsu WL. IASL valence-Arousal analysis system at IALP 2016 shared task: Dimensional sentiment analysis for Chinese words. 於 Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 297-299. 7875990 https://doi.org/10.1109/IALP.2016.7875990
Hsieh, Yu Lun ; Wang, Chen Ann ; Wu, Ying Wei ; Chang, Yung Chun ; Hsu, Wen Lian. / IASL valence-Arousal analysis system at IALP 2016 shared task : Dimensional sentiment analysis for Chinese words. Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 頁 297-299
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abstract = "Sentiment lexicons with valence-Arousal ratings are useful resources for the development of dimensional sentiment applications. In order to solve the significant lack of Chinese valence and arousal lexicons, the objective of the DSAW is to automatically acquire the valence-Arousal ratings of Chinese affective words. In this task, we develop a novel approach that integrate word embeddings into a graph-based model with K-Nearest Neighbor to identify both valence and arousal dimensions. We also propose to use character embeddings to represent unseen words, which is a major challenge in collecting large corpora. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese words with 0.847 and 1.281 mean absolute error (MAE) for valence and arousal respectively.",
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