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

Youngseok Choi, Habin Lee

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)
75 Downloads (Pure)


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.

Publisher Statement: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Original languageEnglish
Pages (from-to)993-1012
Number of pages20
JournalInformation Systems Frontiers
Issue number5
Early online date9 Mar 2017
Publication statusPublished - Oct 2017


  • Sentiment classification
  • Opinion mining
  • Data properties
  • Comparative analysis
  • Sentiment orientation approach
  • Machine learning approach


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