Intensive maximum entropy model for sentiment classification of short text

Yanghui RAO; Jun LI; Xiyun XIANG; Haoran XIE

doi:10.1007/978-3-319-22324-7_4

Intensive maximum entropy model for sentiment classification of short text

Yanghui RAO, Jun LI^*, Xiyun XIANG, Haoran XIE

^*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2015 International Workshops
Editors	Yoshiharu ISHIKAWA, Sarana NUTANONG, An LIU, Tieyun QIAN, Muhammad Aamir CHEEMA
Place of Publication	Switzerland
Publisher	Springer, Cham
Pages	42-51
Number of pages	10
ISBN (Electronic)	9783319223247
ISBN (Print)	9783319223230
DOIs	https://doi.org/10.1007/978-3-319-22324-7_4
Publication status	Published - 2015
Externally published	Yes
Event	The 20th International Conference on Database Systems for Advanced Applications - Hanoi, Viet Nam Duration: 20 Apr 2015 → 23 Apr 2015 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40103&copyownerid=3190

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9052
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 20th International Conference on Database Systems for Advanced Applications
Abbreviated title	DASFAA 2015
Country/Territory	Viet Nam
City	Hanoi
Period	20/04/15 → 23/04/15
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40103&copyownerid=3190

Bibliographical note

The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper.

Funding

The research described in this paper has been supported by “the Fundamental Research Funds for the Central Universities” (46000-31121401), and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).

Keywords

Intensive maximum entropy model
Sentiment classification
Short text analysis

Access to Document

10.1007/978-3-319-22324-7_4

Cite this

RAO, Y., LI, J., XIANG, X., & XIE, H. (2015). Intensive maximum entropy model for sentiment classification of short text. In Y. ISHIKAWA, S. NUTANONG, A. LIU, T. QIAN, & M. A. CHEEMA (Eds.), Database Systems for Advanced Applications - DASFAA 2015 International Workshops (pp. 42-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9052). Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_4

RAO, Yanghui ; LI, Jun ; XIANG, Xiyun et al. / Intensive maximum entropy model for sentiment classification of short text. Database Systems for Advanced Applications - DASFAA 2015 International Workshops. editor / Yoshiharu ISHIKAWA ; Sarana NUTANONG ; An LIU ; Tieyun QIAN ; Muhammad Aamir CHEEMA. Switzerland : Springer, Cham, 2015. pp. 42-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3cfc67a5137d49edb8dcfc5db6976483,

title = "Intensive maximum entropy model for sentiment classification of short text",

abstract = "The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.",

keywords = "Intensive maximum entropy model, Sentiment classification, Short text analysis",

author = "Yanghui RAO and Jun LI and Xiyun XIANG and Haoran XIE",

note = "The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper.; The 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015 ; Conference date: 20-04-2015 Through 23-04-2015",

year = "2015",

doi = "10.1007/978-3-319-22324-7_4",

language = "English",

isbn = "9783319223230",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Cham",

pages = "42--51",

editor = "Yoshiharu ISHIKAWA and Sarana NUTANONG and An LIU and Tieyun QIAN and CHEEMA, {Muhammad Aamir}",

booktitle = "Database Systems for Advanced Applications - DASFAA 2015 International Workshops",

url = "http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40103&copyownerid=3190",

}

RAO, Y, LI, J, XIANG, X & XIE, H 2015, Intensive maximum entropy model for sentiment classification of short text. in Y ISHIKAWA, S NUTANONG, A LIU, T QIAN & MA CHEEMA (eds), Database Systems for Advanced Applications - DASFAA 2015 International Workshops. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9052, Springer, Cham, Switzerland, pp. 42-51, The 20th International Conference on Database Systems for Advanced Applications, Hanoi, Viet Nam, 20/04/15. https://doi.org/10.1007/978-3-319-22324-7_4

Intensive maximum entropy model for sentiment classification of short text. / RAO, Yanghui; LI, Jun; XIANG, Xiyun et al.
Database Systems for Advanced Applications - DASFAA 2015 International Workshops. ed. / Yoshiharu ISHIKAWA; Sarana NUTANONG; An LIU; Tieyun QIAN; Muhammad Aamir CHEEMA. Switzerland: Springer, Cham, 2015. p. 42-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9052).

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

TY - GEN

T1 - Intensive maximum entropy model for sentiment classification of short text

AU - RAO, Yanghui

AU - LI, Jun

AU - XIANG, Xiyun

AU - XIE, Haoran

N1 - The authors are thankful to the anonymous reviewers for their constructive comments and suggestions on an earlier version of this paper.

PY - 2015

Y1 - 2015

N2 - The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.

AB - The rapid development of social media services has facilitated the communication of opinions through microblogs/tweets, instantmessages, online news, and so forth. This article concentrates on the mining of emotions evoked by short text materials. Compared to the classical sentiment analysis from long text, sentiment analysis of short text is sometimes more meaningful in social media. We propose an intensive maximum entropy model for sentiment classification, which generates the probability of sentiments conditioned to short text by employing intensive feature functions. Experimental evaluations using real-world data validate the effectiveness of the proposed model on sentiment classification of short text.

KW - Intensive maximum entropy model

KW - Sentiment classification

KW - Short text analysis

UR - http://www.scopus.com/inward/record.url?scp=84949989034&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-22324-7_4

DO - 10.1007/978-3-319-22324-7_4

M3 - Conference paper (refereed)

AN - SCOPUS:84949989034

SN - 9783319223230

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 42

EP - 51

BT - Database Systems for Advanced Applications - DASFAA 2015 International Workshops

A2 - ISHIKAWA, Yoshiharu

A2 - NUTANONG, Sarana

A2 - LIU, An

A2 - QIAN, Tieyun

A2 - CHEEMA, Muhammad Aamir

PB - Springer, Cham

CY - Switzerland

T2 - The 20th International Conference on Database Systems for Advanced Applications

Y2 - 20 April 2015 through 23 April 2015

ER -

RAO Y, LI J, XIANG X, XIE H. Intensive maximum entropy model for sentiment classification of short text. In ISHIKAWA Y, NUTANONG S, LIU A, QIAN T, CHEEMA MA, editors, Database Systems for Advanced Applications - DASFAA 2015 International Workshops. Switzerland: Springer, Cham. 2015. p. 42-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2015 Jul 30. doi: 10.1007/978-3-319-22324-7_4

Intensive maximum entropy model for sentiment classification of short text

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this