FCE-SVM: a new cluster based ensemble method for opinion mining from social media

Gang Wang, Daqing Zheng (), Shanlin Yang and Jian Ma
Additional contact information
Gang Wang: Hefei University of Technology
Daqing Zheng: SUFE
Shanlin Yang: Hefei University of Technology
Jian Ma: City University of Hong Kong

Information Systems and e-Business Management, 2018, vol. 16, issue 4, No 1, 742 pages

Abstract: Abstract Opinion mining aiming to automatically detect subjective information has raised more and more interests from both academic and industry fields in recent years. In order to enhance the performance of opinion mining, some ensemble methods have been investigated and proven to be effective theoretically and empirically. However, cluster based ensemble method is paid less attention to in the area of opinion mining. In this paper, a new cluster based ensemble method, FCE-SVM, is proposed for opinion mining from social media. Based on the philosophy of divide and conquer, FCE-SVM uses fuzzy clustering module to generate different training sub datasets in the first stage. Then, base learners are trained based on different training datasets in the second stage. Finally, fusion module is employed to combine the results of based learners. Moreover, the multi-domain opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that FCE-SVM gets the best performance through reducing bias and variance simultaneously. These results illustrate that FCE-SVM can be used as a viable method for opinion mining.

Keywords: Opinion mining; Ensemble learning; Cluster; SVM; Social media (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10257-017-0352-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infsem:v:16:y:2018:i:4:d:10.1007_s10257-017-0352-0

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/10257

DOI: 10.1007/s10257-017-0352-0

Access Statistics for this article

Information Systems and e-Business Management is currently edited by Jörg Becker and Michael J. Shaw

More articles in Information Systems and e-Business Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().