Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FUZZ-IEEE.2019.8858916 | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
Keywords | Field | DocType |
drug reviews,automatic sentiment classification,fuzzy rough feature selection,sentiment analysis,healthcare,public health,users drug experience,feature space | Feature vector,Noisy data,Feature selection,Computer science,Sentiment analysis,Fuzzy logic,Feature extraction,Large numbers,Artificial intelligence,Machine learning,Overhead (business) | Conference |
ISSN | ISBN | Citations |
1544-5615 | 978-1-5386-1729-8 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianhua Chen | 1 | 2 | 1.04 |
| Pan Su | 2 | 82 | 11.72 |
| Changjing Shang | 3 | 212 | 34.92 |
| Richard Hill | 4 | 1 | 0.68 |
| Hengshan Zhang | 5 | 1 | 0.35 |
| Qiang Shen | 6 | 1878 | 94.48 |