Evaluation of weighted graph and neural network models on predicting the valence-Arousal ratings of Chinese words

Wei Chieh Chou, Chin Kui Lin, Yih-Ru Wang, Yuan Fu Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Asian Language Processing, IALP 2016
EditorsMinghui Dong, Chung-Hsien Wu, Yanfeng Lu, Haizhou Li, Yuen-Hsien Tseng, Liang-Chih Yu, Lung-Hao Lee
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages168-171
Number of pages4
ISBN (Electronic)9781509009213
DOIs
StatePublished - 10 Mar 2017
Event20th International Conference on Asian Language Processing, IALP 2016 - Tainan, Taiwan
Duration: 21 Nov 201623 Nov 2016

Publication series

NameProceedings of the 2016 International Conference on Asian Language Processing, IALP 2016

Conference

Conference20th International Conference on Asian Language Processing, IALP 2016
CountryTaiwan
CityTainan
Period21/11/1623/11/16

Keywords

  • Neural Network
  • Sentiment Analysis
  • Weighted Graph Model
  • Word Embedding

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