Sentiment classification using negative and intensive sentiment supplement information

Xingming CHEN; Yanghui RAO; Haoran XIE; Fu Lee WANG; Yingchao ZHAO; Jian YIN

doi:10.1007/s41019-019-0094-8

Sentiment classification using negative and intensive sentiment supplement information

Xingming CHEN, Yanghui RAO^*, Haoran XIE, Fu Lee WANG, Yingchao ZHAO, Jian YIN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

26 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	109-118
Number of pages	10
Journal	Data Science and Engineering
Volume	4
Issue number	2
Early online date	15 Jun 2019
DOIs	https://doi.org/10.1007/s41019-019-0094-8
Publication status	Published - Jun 2019
Externally published	Yes

Bibliographical note

This 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.

Keywords

Intensive words
Negative words
Sentiment supplementary information

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10.1007/s41019-019-0094-8

Cite this

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title = "Sentiment classification using negative and intensive sentiment supplement information",

abstract = "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.",

keywords = "Intensive words, Negative words, Sentiment supplementary information",

author = "Xingming CHEN and Yanghui RAO and Haoran XIE and WANG, {Fu Lee} and Yingchao ZHAO and Jian YIN",

note = "This 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{\textquoteright}s Research Fund 2018–19 (FLASS/DRF/IDS-3), Top-Up Fund (TFG-04) for General Research Fund/Early Career Scheme of the Dean{\textquoteright}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. ",

year = "2019",

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doi = "10.1007/s41019-019-0094-8",

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AU - RAO, Yanghui

AU - XIE, Haoran

AU - WANG, Fu Lee

AU - ZHAO, Yingchao

AU - YIN, Jian

N1 - This 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.

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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.

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Sentiment classification using negative and intensive sentiment supplement information

Abstract

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Keywords

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