@inproceedings{lin-etal-2017-cial,
title = "{CIAL} at {IJCNLP}-2017 Task 2: An Ensemble Valence-Arousal Analysis System for {C}hinese Words and Phrases",
author = "Lin, Zheng-Wen and
Chang, Yung-Chun and
Wang, Chen-Ann and
Hsieh, Yu-Lun and
Hsu, Wen-Lian",
editor = "Liu, Chao-Hong and
Nakov, Preslav and
Xue, Nianwen",
booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
month = dec,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-4015",
pages = "95--99",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases
%A Lin, Zheng-Wen
%A Chang, Yung-Chun
%A Wang, Chen-Ann
%A Hsieh, Yu-Lun
%A Hsu, Wen-Lian
%Y Liu, Chao-Hong
%Y Nakov, Preslav
%Y Xue, Nianwen
%S Proceedings of the IJCNLP 2017, Shared Tasks
%D 2017
%8 December
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F lin-etal-2017-cial
%X 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.
%U https://aclanthology.org/I17-4015
%P 95-99
Markdown (Informal)
[CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases](https://aclanthology.org/I17-4015) (Lin et al., IJCNLP 2017)
ACL