Abstract
In this study, an automatic classification method based on the sentiment polarity of text is proposed. This method uses two sentiment dictionaries from different sources: The Chinese sentiment dictionary CSWN that integrates Chinese WordNet with SentiWordNet, and the sentiment dictionary obtained from a training corpus labeled with sentiment polarities. In this study, the sentiment polarity of text is analyzed using these two dictionaries, a mixed-rule approach, and a statistics-based prediction model. The proposed method is used to analyze a test corpus provided by the Topic-Based Chinese Message Polarity Classification task of SIGHAN-8, and the F1-measure value is tested at 0.62.
Cite
CITATION STYLE
Chang, T. H., Chen, C. H., Lin, M. J., & Wang, S. Y. (2015). CT-SPA: Text sentiment polarity prediction model using semi-Automatically expanded sentiment lexicon. In Proceedings of the 8th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, ACL IJCNLP 2015 (pp. 164–170). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w15-3125
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
