Sentiment analysis for online reviews using conditional random fields and support vector machines
Document Type
Article
Publication Date
6-1-2020
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.
Publication Title
Electronic Commerce Research
Volume
20
Issue
2
First Page
343
Last Page
360
Digital Object Identifier (DOI)
10.1007/s10660-019-09354-7
ISSN
13895753
E-ISSN
15729362
Citation Information
Xia, H., Yang, Y., Pan, X. et al. Sentiment analysis for online reviews using conditional random fields and support vector machines. Electron Commer Res 20, 343–360 (2020). https://doi.org/10.1007/s10660-019-09354-7