Applied Intelligence

, Volume 48, Issue 5, pp 1218–1232 | Cite as

Hybrid sentiment classification on twitter aspect-based sentiment analysis

  • Nurulhuda Zainuddin
  • Ali Selamat
  • Roliana Ibrahim
Article
  • 355 Downloads

Abstract

Social media sites and applications, including Facebook, YouTube, Twitter and blogs, have become major social media attractions today. The huge amount of information from this medium has become an attractive resource for organisations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. Early work on sentiment analysis approached this problem at a document-level, where the overall sentiment was identified, rather than the details of the sentiment. This research took into account the use of an aspect-based sentiment analysis on Twitter in order to perform a finer-grained analysis. A new hybrid sentiment classification for Twitter is proposed by embedding a feature selection method. A comparison of the accuracy of the classification by the principal component analysis (PCA), latent semantic analysis (LSA), and random projection (RP) feature selection methods are presented in this paper. Furthermore, the hybrid sentiment classification was validated using Twitter datasets to represent different domains, and the evaluation with different classification algorithms also demonstrated that the new hybrid approach produced meaningful results. The implementations showed that the new hybrid sentiment classification was able to improve the accuracy performance from the existing baseline sentiment classification methods by 76.55, 71.62 and 74.24%, respectively.

Keywords

Aspect-based sentiment classification Feature selection Principal component analysis Support vector machine Hybrid approach 

Notes

Acknowledgments

The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot- 02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Nurulhuda Zainuddin
    • 1
  • Ali Selamat
    • 1
    • 2
    • 3
  • Roliana Ibrahim
    • 1
  1. 1.Faculty of ComputingUniversiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  2. 2.MAGIC-X (Media and Game Innovation Centre of Excellence)Universiti Teknologi Malaysia (UTM)Johor BahruMalaysia
  3. 3.Centre for Basic and Applied Research, Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

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