Sentiment analysis for online reviews using conditional random fields and support vector machines

Huosong Xia (), Yitai Yang (), Xiaoting Pan (), Zuopeng Zhang () and Wuyue An ()
Additional contact information
Huosong Xia: Wuhan Textile University
Yitai Yang: Wuhan Textile University
Xiaoting Pan: Wuhan Textile University
Zuopeng Zhang: University of North Florida
Wuyue An: Wuhan Textile University

Electronic Commerce Research, 2020, vol. 20, issue 2, No 7, 343-360

Abstract: Abstract Sentiment analysis of online reviews is an important way of mining useful information from the Internet. Despite several advantages, the accuracy of sentiment analysis based on a domain dictionary relies on the comprehensiveness and accuracy of the dictionary. Instead of creating a domain dictionary, we propose an approach for online review sentiment classification, which uses a conditional random field algorithm to extract the emotional characteristics from fragments of the review. The characteristic (feature) words are then weighted asymmetrically before a support vector machine classifier is used to obtain the sentiment orientation of the review. In our experiments, the average accuracy reached 90%, showing that using sentiment feature fragments instead of whole reviews and weighting the characteristic words asymmetrically can improve the sentiment classification accuracy.

Keywords: Sentiment analysis; Conditional random field; Online review; Support vector machine (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10660-019-09354-7 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:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09354-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10660

DOI: 10.1007/s10660-019-09354-7

Access Statistics for this article

Electronic Commerce Research is currently edited by James Westland

More articles in Electronic Commerce Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().