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Interactions Between Term Weighting and Feature Selection Methods on the Sentiment Analysis of Turkish Reviews

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9624))

Abstract

Term weighting methods assign appropriate weights to the terms in a document so that more important terms receive higher weights for the text representation. In this study, we consider four term weighting and three feature selection methods and investigate how these term weighting methods respond to the reduced text representation. We conduct experiments on five Turkish review datasets so that we can establish baselines and compare the performance of these term weighting methods. We test these methods on the English reviews so that we can identify their differences with the Turkish reviews. We show that both tf and tp weighting methods are the best for the Turkish, while tp is the best for the English reviews. When feature selection is applied, tf * idf method with DFD and χ2 has the highest accuracies for the Turkish, while tf * idf and tp methods with χ2 have the best performance for the English reviews.

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Notes

  1. 1.

    http://nltk.org.

  2. 2.

    http://www.cs.waikato.ac.nz/ml/weka.

  3. 3.

    http://www.win.tue.nl/~mpechen/projects/smm/#Datasets.

  4. 4.

    http://www.cs.cornell.edu/people/pabo/movie-review-data/.

  5. 5.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

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Acknowledgements

This study was supported by Çukurova University Academic Research Project Unit under the grant no FDK-2015-3833 and by The Scientific and Technological Research Council of Turkey (TÜBİTAK) scholarship TUBITAK-2214-A.

Author information

Authors and Affiliations

  1. Department of Mathematics, Mustafa Kemal University, Hatay, Turkey

    Tuba Parlar

  2. Department of Computer Engineering, Cukurova University, Adana, Turkey

    Selma Ayşe Özel

  3. School of Computer Science, University of Guelph, Guelph, Canada

    Fei Song

Authors
  1. Tuba Parlar
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  2. Selma Ayşe Özel
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  3. Fei Song
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Corresponding author

Correspondence to Tuba Parlar .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Parlar, T., Özel, S.A., Song, F. (2018). Interactions Between Term Weighting and Feature Selection Methods on the Sentiment Analysis of Turkish Reviews. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-75487-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75486-4

  • Online ISBN: 978-3-319-75487-1

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