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dc.contributor.authorChou, Wei-Chiehen_US
dc.contributor.authorLin, Chin-Kuien_US
dc.contributor.authorWang, Yih-Ruen_US
dc.contributor.authorLiao, Yuan-Fuen_US
dc.date.accessioned2018-08-21T05:56:43Z-
dc.date.available2018-08-21T05:56:43Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn2159-1962en_US
dc.identifier.urihttp://hdl.handle.net/11536/146561-
dc.description.abstractAutomatic sentiment information extraction of social network articles has many essential applications. Following the valence-arousal space framework, in this paper, two approaches including (1) a weighted graph (WG) and (2) a neural network (NN) model that could predict the valence-arousal ratings of words are evaluated on the Chinese valence-arousal words (CVAW) database provide by the IALP-2016 shared task. According the official evaluation results, our NN systems achieved (0.621,1.165) MAEs and (0.853,0.631) PCCs for valence and arousal predictions. Compared with the results of other participants, the performance of our systems are quite nice.en_US
dc.language.isoen_USen_US
dc.subjectWord Embeddingen_US
dc.subjectWeighted Graph Modelen_US
dc.subjectNeural Networken_US
dc.subjectSentiment Analysisen_US
dc.titleEvaluation of Weighted Graph and Neural Network Models on Predicting the Valence-Arousal Ratings of Chinese Wordsen_US
dc.typeProceedings Paperen_US
dc.identifier.journalPROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP)en_US
dc.citation.spage168en_US
dc.citation.epage171en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000401528000040en_US
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