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Data properties and the performance of sentiment classification for electronic commerce applications

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  • Youngseok Choi

    (Coventry University)

  • Habin Lee

    (Brunel University London)

Abstract

Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

Suggested Citation

  • Youngseok Choi & Habin Lee, 2017. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 19(5), pages 993-1012, October.
  • Handle: RePEc:spr:infosf:v:19:y:2017:i:5:d:10.1007_s10796-017-9741-7
    DOI: 10.1007/s10796-017-9741-7
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    References listed on IDEAS

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    Cited by:

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    7. Luvai Motiwalla & Amit V. Deokar & Surendra Sarnikar & Angelika Dimoka, 2019. "Leveraging Data Analytics for Behavioral Research," Information Systems Frontiers, Springer, vol. 21(4), pages 735-742, August.
    8. Vijayan Sugumaran & T. V. Geetha & D. Manjula & Hema Gopal, 2017. "Guest Editorial: Computational Intelligence and Applications," Information Systems Frontiers, Springer, vol. 19(5), pages 969-974, October.
    9. Zheng Xie & Guannan Liu & Jinming Qu & Junjie Wu & Hong Li, 2022. "Identifying Structural Holes for Sentiment Classification," Information Systems Frontiers, Springer, vol. 24(5), pages 1735-1751, October.

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