Predicting the semantic orientation of terms in E-HowNet

Authors:
Cheng-Ru Li;Chi-Hsin Yu;Hsin-Hsi Chen
Affiliations:
National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan
Venue:
ROCLING '11 Proceedings of the 23rd Conference on Computational Linguistics and Speech Processing
Year:
2011

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Abstract

The semantic orientation of terms is fundamental for sentiment analysis in sentence and document levels. Although some Chinese sentiment dictionaries are available, how to predict the orientation of terms automatically is still important. In this paper, we predict the semantic orientation of terms of E-HowNet. We extract many useful features from different sources to represent a Chinese term in E-HowNet, and use a supervised machine learning algorithm to predict its orientation. Our experimental result showed that the proposed approach can achieve 92.33% accuracy, which is comparable to the accuracy of human taggers.