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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Asian Language Processing, IALP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-299
Number of pages3
ISBN (Electronic)9781509009213
DOIs
Publication statusPublished - Mar 10 2017
Externally publishedYes
Event20th International Conference on Asian Language Processing, IALP 2016 - Tainan, Taiwan
Duration: Nov 21 2016Nov 23 2016

Conference

Conference20th International Conference on Asian Language Processing, IALP 2016
CountryTaiwan
CityTainan
Period11/21/1611/23/16

Fingerprint

dimensional analysis
systems analysis
rating
lack
evaluation
resources

ASJC Scopus subject areas

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

Cite this

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. In Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016 (pp. 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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. in Proceedings of the 2016 International Conference on Asian Language Processing, IALP 2016., 7875990, Institute of Electrical and Electronics Engineers Inc., pp. 297-299, 20th International Conference on Asian Language Processing, IALP 2016, Tainan, Taiwan, 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. In 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. pp. 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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