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.
Bibliographical noteThis research has been supported in part by the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), the Interdisciplinary Research Scheme of the Dean’s Research Fund 2018–19 (FLASS/DRF/IDS-3), Top-Up Fund (TFG-04) for General Research Fund/Early Career Scheme of the Dean’s Research Fund (DRF) 2018–19 and the Internal Research Grant (RG 90/2018–2019R) of The Education University of Hong Kong, the National Key R&D Program of China (2018YFB1004404), Key R&D Program of Guangdong Province (2018B010107005), and National Natural Science Foundation of China (U1711262, U1401256, U1501252, U1611264, U1711261). The preliminary version of this article has been published in APWeb-WAIM 2018.
The National Natural Science Foundation of China (61502545, U1711262, U1611264), a grant from Research Grants Council of Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Innovation and Technology Fund (Project No. GHP/022/17GD) from the Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region.
- Intensive words
- Negative words
- Sentiment supplementary information