Sentiment Classification of Drug Reviews Using Fuzzy-rough Feature Selection

Tianhua Chen, Pan Su, Changjing Shang, Richard Hill, Hengshan Zhang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sentiment analysis mines people’s opinions and attitudes regarding a certain issue from source materials. Recently, it has drawn significant attention in a number of application areas. The sentiment analysis of healthcare in general and that of users’ drug experience in particular could shed significant light on how to improve public health and make the right decisions. However, one of the major challenges in sentiment classification lies in the very large number of extracted features. Fuzzy-rough feature selection provides a means by which discrete or real-valued noisy data can be effectively reduced without human intervention. This paper proposes an implementation for automatic sentiment classification of drug reviews employing fuzzy rough feature selection. Experimental results demonstrate that the employment of fuzzy-rough feature selection can indeed significantly reduce the complexity of feature space and the classification run-time overheads while maintaining classification accuracy.
Original languageEnglish
Title of host publication2019 IEEE International Conferences on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781538617281
ISBN (Print)9781538617298
DOIs
Publication statusPublished - 10 Oct 2019
Event2019 IEEE International Conference on Fuzzy Systems - New Orleans, United States
Duration: 23 Jun 201926 Jun 2019

Conference

Conference2019 IEEE International Conference on Fuzzy Systems
CountryUnited States
CityNew Orleans
Period23/06/1926/06/19

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Chen, T., Su, P., Shang, C., Hill, R., Zhang, H., & Shen, Q. (2019). Sentiment Classification of Drug Reviews Using Fuzzy-rough Feature Selection. In 2019 IEEE International Conferences on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2019.8858916