TY - JOUR
T1 - Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
AU - Chen, Xingming
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Wang, Fu Lee
AU - Zhao, Yingchao
AU - Yin, Jian
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Intensive words
KW - Negative words
KW - Sentiment supplementary information
UR - http://www.scopus.com/inward/record.url?scp=85067856704&partnerID=8YFLogxK
U2 - 10.1007/s41019-019-0094-8
DO - 10.1007/s41019-019-0094-8
M3 - Article
AN - SCOPUS:85067856704
SN - 2364-1185
VL - 4
SP - 109
EP - 118
JO - Data Science and Engineering
JF - Data Science and Engineering
IS - 2
ER -