CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases

Zheng-Wen Lin, Yung-Chun Chang, Chen-Ann Wang, Yu-Lun Hsieh, Wen-Lian Hsu


Abstract
Sentiment lexicon is very helpful in dimensional sentiment applications. Because of countless Chinese words, developing a method to predict unseen Chinese words is required. The proposed method can handle both words and phrases by using an ADVWeight List for word prediction, which in turn improves our performance at phrase level. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese phrases. The Mean Absolute Error (MAE) and Pearson’s Correlation Coefficient (PCC) for Valence are 0.723 and 0.835, respectively, and those for Arousal are 0.914 and 0.756, respectively.
Anthology ID:
I17-4015
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Editors:
Chao-Hong Liu, Preslav Nakov, Nianwen Xue
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
95–99
Language:
URL:
https://aclanthology.org/I17-4015
DOI:
Bibkey:
Cite (ACL):
Zheng-Wen Lin, Yung-Chun Chang, Chen-Ann Wang, Yu-Lun Hsieh, and Wen-Lian Hsu. 2017. CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 95–99, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases (Lin et al., IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-4015.pdf