Twitter sentiment analysis: A bootstrap ensemble framework

Ammar Hassan, Ahmed Abbasi, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Scopus citations

Abstract

Twitter sentiment analysis has become widely popular. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a text analytics framework is proposed for Twitter sentiment analysis. The framework uses an elaborate bootstrapping ensemble to quell class imbalance, sparsity, and representational richness issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, the bootstrapping ensemble framework is able to build sentiment time series that are better able to reflect events eliciting strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premiere social media platforms, the results have important implications for social media analytics and social intelligence.

Original languageEnglish (US)
Title of host publicationProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013
Pages357-364
Number of pages8
DOIs
StatePublished - Dec 1 2013
Event2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013 - Washington, DC, United States
Duration: Sep 8 2013Sep 14 2013

Publication series

NameProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013

Other

Other2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013
CountryUnited States
CityWashington, DC
Period9/8/139/14/13

Keywords

  • Machine learning
  • Opinion mining
  • Sentiment analysis
  • Social media analytics
  • Text mining

ASJC Scopus subject areas

  • Software

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  • Cite this

    Hassan, A., Abbasi, A., & Zeng, D. (2013). Twitter sentiment analysis: A bootstrap ensemble framework. In Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 (pp. 357-364). [6693353] (Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013). https://doi.org/10.1109/SocialCom.2013.56