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

Data properties and the performance of sentiment classification for electronic commerce applications
Data properties and the performance of sentiment classification for electronic commerce applications
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.
Comparative analysis, Data properties, Machine learning approach, Opinion mining, Sentiment classification, Sentiment orientation approach
1572-9419
993-1012
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d

Choi, Youngseok and Lee, Habin (2017) Data properties and the performance of sentiment classification for electronic commerce applications. Information Systems Frontiers, 19 (5), 993-1012. (doi:10.1007/s10796-017-9741-7).

Record type: Article

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.

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Choi-Lee 2017 Article Data Properties And The Performance - Version of Record
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More information

Accepted/In Press date: 1 January 2017
e-pub ahead of print date: 9 March 2017
Published date: October 2017
Keywords: Comparative analysis, Data properties, Machine learning approach, Opinion mining, Sentiment classification, Sentiment orientation approach

Identifiers

Local EPrints ID: 437730
URI: http://eprints.soton.ac.uk/id/eprint/437730
ISSN: 1572-9419
PURE UUID: eacd2e80-6385-46b4-85be-e04bf05b0578
ORCID for Youngseok Choi: ORCID iD orcid.org/0000-0001-9842-5231

Catalogue record

Date deposited: 13 Feb 2020 17:30
Last modified: 07 Oct 2020 02:27

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Contributors

Author: Youngseok Choi ORCID iD
Author: Habin Lee

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