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 ProceedingsConference paper (refereed)Researchpeer-review

7 Citations (Scopus)


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 languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - DASFAA 2015 International Workshops
EditorsYoshiharu ISHIKAWA, Sarana NUTANONG, An LIU, Tieyun QIAN, Muhammad Aamir CHEEMA
Place of PublicationSwitzerland
PublisherSpringer, Cham
Number of pages10
ISBN (Electronic)9783319223247
ISBN (Print)9783319223230
Publication statusPublished - 2015
Externally publishedYes
EventThe 20th International Conference on Database Systems for Advanced Applications - Hanoi, Viet Nam
Duration: 20 Apr 201523 Apr 2015

Publication series

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


ConferenceThe 20th International Conference on Database Systems for Advanced Applications
Abbreviated titleDASFAA 2015
Country/TerritoryViet Nam
Internet address

Bibliographical note

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


  • Intensive maximum entropy model
  • Sentiment classification
  • Short text analysis


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