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

29 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

Funding

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

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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.",

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

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AU - CHEN, Xingming

AU - RAO, Yanghui

AU - XIE, Haoran

AU - WANG, Fu Lee

AU - ZHAO, Yingchao

AU - YIN, Jian

PY - 2019/6

Y1 - 2019/6

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

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DO - 10.1007/s41019-019-0094-8

M3 - Journal Article (refereed)

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 -

Sentiment classification using negative and intensive sentiment supplement information

Abstract

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Keywords

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