Please use this identifier to cite or link to this item:
Title: Sentiment classification using negative and intensive sentiment supplement information
Author(s): Zhao, Yingchao 
Xie, Haoran 
Wang, Philips Fu Lee 
Author(s): Chen, X.
Rao, Y.
Yin, J.
Issue Date: 2019
Publisher: Springer
Journal: Data Science and Engineering 
Volume: 4
Issue: 2
Start page: 109
End page: 118
Traditional methods of annotating the sentiment of an unlabeled document are based on sentiment lexicons or machine learning algorithms, which have shown low computational cost or competitive performance. However, these methods ignore the semantic composition problem displaying in several ways such as negative reversing and intensification. In this paper, we propose a new method for sentiment classification using negative and intensive sentiment supplementary information, so as to exploit the linguistic feature of negative and intensive words in conjunction with the context information. Particularly, our method can solve the domain-specific problem without relying on the external sentiment lexicons. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed method.
DOI: 10.1007/s41019-019-0094-8
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

Files in This Item:
File Description SizeFormat
View Online90 BHTMLView/Open
SFX Query Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.