標題: Evaluation of Weighted Graph and Neural Network Models on Predicting the Valence-Arousal Ratings of Chinese Words
作者: Chou, Wei-Chieh
Lin, Chin-Kui
Wang, Yih-Ru
Liao, Yuan-Fu
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Word Embedding;Weighted Graph Model;Neural Network;Sentiment Analysis
公開日期: 1-一月-2016
摘要: Automatic 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.
URI: http://hdl.handle.net/11536/146561
ISSN: 2159-1962
期刊: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP)
起始頁: 168
結束頁: 171
顯示於類別:會議論文