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

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...

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Bibliographic Details
Published in2016 International Conference on Asian Language Processing (IALP) pp. 297 - 299
Main Authors Yu-Lun Hsieh, Chen-Ann Wang, Ying-Wei Wu, Yung-Chun Chang, Wen-Lian Hsu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Data mining
Emotion recognition
Sentiment analysis
Support vector machines
Training
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Summary: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.
DOI:10.1109/IALP.2016.7875990