Sentiment classification via supplementary information modeling

Zenan  XU; Yetao FU; Xingming CHEN; Yanghui RAO; Haoran XIE; Fu Lee WANG; Yang PENG

doi:10.1007/978-3-319-96890-2_5

Sentiment classification via supplementary information modeling

Zenan XU, Yetao FU, Xingming CHEN, Yanghui RAO^*, Haoran XIE, Fu Lee WANG, Yang PENG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research

2 Citations (Scopus)

Abstract

Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.

Original language	English
Title of host publication	Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I
Editors	Yi CAI, Yoshiharu ISHIKAWA, Jianliang XU
Publisher	Springer International Publishing AG
Pages	54-62
Number of pages	9
ISBN (Electronic)	9783319968902
ISBN (Print)	9783319968896
DOIs	https://doi.org/10.1007/978-3-319-96890-2_5
Publication status	Published - 2018
Externally published	Yes
Event	Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data - Macau, Macao Duration: 23 Jul 2018 → 25 Jul 2018 http://conferences.cis.umac.mo/apwebwaim2018/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10987
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data
Abbreviated title	APWeb-WAIM 2018
Country/Territory	Macao
City	Macau
Period	23/07/18 → 25/07/18
Internet address	http://conferences.cis.umac.mo/apwebwaim2018/

Bibliographical note

The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), Guangdong Science and Technology Program grant (2017A050506025), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong.

Access to Document

10.1007/978-3-319-96890-2_5

Cite this

XU, Z., FU, Y., CHEN, X., RAO, Y., XIE, H., WANG, F. L., & PENG, Y. (2018). Sentiment classification via supplementary information modeling. In Y. CAI, Y. ISHIKAWA, & J. XU (Eds.), Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I (pp. 54-62). (Lecture Notes in Computer Science; Vol. 10987). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-96890-2_5

XU, Zenan ; FU, Yetao ; CHEN, Xingming et al. / Sentiment classification via supplementary information modeling. Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I. editor / Yi CAI ; Yoshiharu ISHIKAWA ; Jianliang XU. Springer International Publishing AG, 2018. pp. 54-62 (Lecture Notes in Computer Science).

@inproceedings{258289609cf24bb1934542716afe3c46,

title = "Sentiment classification via supplementary information modeling",

abstract = "Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.",

author = "Zenan XU and Yetao FU and Xingming CHEN and Yanghui RAO and Haoran XIE and WANG, {Fu Lee} and Yang PENG",

note = "The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), Guangdong Science and Technology Program grant (2017A050506025), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong.; Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",

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doi = "10.1007/978-3-319-96890-2_5",

language = "English",

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booktitle = "Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I",

address = "Switzerland",

url = "http://conferences.cis.umac.mo/apwebwaim2018/",

}

XU, Z, FU, Y, CHEN, X, RAO, Y, XIE, H, WANG, FL & PENG, Y 2018, Sentiment classification via supplementary information modeling. in Y CAI, Y ISHIKAWA & J XU (eds), Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I. Lecture Notes in Computer Science, vol. 10987, Springer International Publishing AG, pp. 54-62, Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, Macau, Macao, 23/07/18. https://doi.org/10.1007/978-3-319-96890-2_5

Sentiment classification via supplementary information modeling. / XU, Zenan ; FU, Yetao; CHEN, Xingming et al.
Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I. ed. / Yi CAI; Yoshiharu ISHIKAWA; Jianliang XU. Springer International Publishing AG, 2018. p. 54-62 (Lecture Notes in Computer Science; Vol. 10987).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research

TY - GEN

T1 - Sentiment classification via supplementary information modeling

AU - XU, Zenan

AU - FU, Yetao

AU - CHEN, Xingming

AU - RAO, Yanghui

AU - XIE, Haoran

AU - WANG, Fu Lee

AU - PENG, Yang

N1 - The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), Guangdong Science and Technology Program grant (2017A050506025), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Internal Research Grant (RG 92/2017-2018R) of The Education University of Hong Kong.

PY - 2018

Y1 - 2018

N2 - Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.

AB - Traditional methods of annotating the sentiment of a document are based on sentiment lexicons, which have been proven quite efficient. However, such methods ignore the effect of supplementary features (e.g., negation and intensity words), while only consider the counts of positive and negative words, the sum of strengths, or the maximum sentiment score over the whole document primarily. In this paper, we propose to use convolutional neural network (CNN) and long short-term memory network (LSTM) to model the role of negation and intensity words, so as to address the limitations of lexicon-based methods. Results show that our model can not only successfully capture the effect of negation and intensity words, but also achieve significant improvements over state-of-the-art deep neural network baselines without supplementary features.

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U2 - 10.1007/978-3-319-96890-2_5

DO - 10.1007/978-3-319-96890-2_5

M3 - Conference paper (refereed)

SN - 9783319968896

T3 - Lecture Notes in Computer Science

SP - 54

EP - 62

BT - Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I

A2 - CAI, Yi

A2 - ISHIKAWA, Yoshiharu

A2 - XU, Jianliang

PB - Springer International Publishing AG

T2 - Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data

Y2 - 23 July 2018 through 25 July 2018

ER -

XU Z, FU Y, CHEN X, RAO Y, XIE H, WANG FL et al. Sentiment classification via supplementary information modeling. In CAI Y, ISHIKAWA Y, XU J, editors, Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, proceedings, part I. Springer International Publishing AG. 2018. p. 54-62. (Lecture Notes in Computer Science). Epub 2018 Jul 19. doi: 10.1007/978-3-319-96890-2_5

Sentiment classification via supplementary information modeling

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