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 w...
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Autor*in:	Chen, Xingming [verfasserIn] Rao, Yanghui Xie, Haoran Wang, Fu Lee Zhao, Yingchao Yin, Jian

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Negative words Intensive words Sentiment supplementary information

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Data science and engineering - Berlin : Springer, 2016, 4(2019), 2 vom: Juni, Seite 109-118
Übergeordnetes Werk:	volume:4 ; year:2019 ; number:2 ; month:06 ; pages:109-118

Links:	Volltext

DOI / URN:	10.1007/s41019-019-0094-8

Katalog-ID:	SPR038061201

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allfields	10.1007/s41019-019-0094-8 doi (DE-627)SPR038061201 (SPR)s41019-019-0094-8-e DE-627 ger DE-627 rakwb eng Chen, Xingming verfasserin aut Sentiment Classification Using Negative and Intensive Sentiment Supplement Information 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213 Rao, Yanghui aut Xie, Haoran aut Wang, Fu Lee aut Zhao, Yingchao aut Yin, Jian aut Enthalten in Data science and engineering Berlin : Springer, 2016 4(2019), 2 vom: Juni, Seite 109-118 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:4 year:2019 number:2 month:06 pages:109-118 https://dx.doi.org/10.1007/s41019-019-0094-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 2 06 109-118
spelling	10.1007/s41019-019-0094-8 doi (DE-627)SPR038061201 (SPR)s41019-019-0094-8-e DE-627 ger DE-627 rakwb eng Chen, Xingming verfasserin aut Sentiment Classification Using Negative and Intensive Sentiment Supplement Information 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213 Rao, Yanghui aut Xie, Haoran aut Wang, Fu Lee aut Zhao, Yingchao aut Yin, Jian aut Enthalten in Data science and engineering Berlin : Springer, 2016 4(2019), 2 vom: Juni, Seite 109-118 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:4 year:2019 number:2 month:06 pages:109-118 https://dx.doi.org/10.1007/s41019-019-0094-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 2 06 109-118
allfields_unstemmed	10.1007/s41019-019-0094-8 doi (DE-627)SPR038061201 (SPR)s41019-019-0094-8-e DE-627 ger DE-627 rakwb eng Chen, Xingming verfasserin aut Sentiment Classification Using Negative and Intensive Sentiment Supplement Information 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213 Rao, Yanghui aut Xie, Haoran aut Wang, Fu Lee aut Zhao, Yingchao aut Yin, Jian aut Enthalten in Data science and engineering Berlin : Springer, 2016 4(2019), 2 vom: Juni, Seite 109-118 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:4 year:2019 number:2 month:06 pages:109-118 https://dx.doi.org/10.1007/s41019-019-0094-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 2 06 109-118
allfieldsGer	10.1007/s41019-019-0094-8 doi (DE-627)SPR038061201 (SPR)s41019-019-0094-8-e DE-627 ger DE-627 rakwb eng Chen, Xingming verfasserin aut Sentiment Classification Using Negative and Intensive Sentiment Supplement Information 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213 Rao, Yanghui aut Xie, Haoran aut Wang, Fu Lee aut Zhao, Yingchao aut Yin, Jian aut Enthalten in Data science and engineering Berlin : Springer, 2016 4(2019), 2 vom: Juni, Seite 109-118 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:4 year:2019 number:2 month:06 pages:109-118 https://dx.doi.org/10.1007/s41019-019-0094-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 2 06 109-118
allfieldsSound	10.1007/s41019-019-0094-8 doi (DE-627)SPR038061201 (SPR)s41019-019-0094-8-e DE-627 ger DE-627 rakwb eng Chen, Xingming verfasserin aut Sentiment Classification Using Negative and Intensive Sentiment Supplement Information 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 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. Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213 Rao, Yanghui aut Xie, Haoran aut Wang, Fu Lee aut Zhao, Yingchao aut Yin, Jian aut Enthalten in Data science and engineering Berlin : Springer, 2016 4(2019), 2 vom: Juni, Seite 109-118 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:4 year:2019 number:2 month:06 pages:109-118 https://dx.doi.org/10.1007/s41019-019-0094-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2019 2 06 109-118
language	English
source	Enthalten in Data science and engineering 4(2019), 2 vom: Juni, Seite 109-118 volume:4 year:2019 number:2 month:06 pages:109-118
sourceStr	Enthalten in Data science and engineering 4(2019), 2 vom: Juni, Seite 109-118 volume:4 year:2019 number:2 month:06 pages:109-118
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Negative words Intensive words Sentiment supplementary information
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container_title	Data science and engineering
authorswithroles_txt_mv	Chen, Xingming @@aut@@ Rao, Yanghui @@aut@@ Xie, Haoran @@aut@@ Wang, Fu Lee @@aut@@ Zhao, Yingchao @@aut@@ Yin, Jian @@aut@@
publishDateDaySort_date	2019-06-01T00:00:00Z
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author	Chen, Xingming
spellingShingle	Chen, Xingming misc Negative words misc Intensive words misc Sentiment supplementary information Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
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topic_title	Sentiment Classification Using Negative and Intensive Sentiment Supplement Information Negative words (dpeaa)DE-He213 Intensive words (dpeaa)DE-He213 Sentiment supplementary information (dpeaa)DE-He213
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title	Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
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title_full	Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
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author_browse	Chen, Xingming Rao, Yanghui Xie, Haoran Wang, Fu Lee Zhao, Yingchao Yin, Jian
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title_sort	sentiment classification using negative and intensive sentiment supplement information
title_auth	Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
abstract	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. © The Author(s) 2019
abstractGer	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. © The Author(s) 2019
abstract_unstemmed	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. © The Author(s) 2019
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title_short	Sentiment Classification Using Negative and Intensive Sentiment Supplement Information
url	https://dx.doi.org/10.1007/s41019-019-0094-8
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author2	Rao, Yanghui Xie, Haoran Wang, Fu Lee Zhao, Yingchao Yin, Jian
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up_date	2024-07-03T15:59:52.092Z
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