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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2019: Intelligent Data Engineering and Automated Learning – IDEAL 2019 pp 257–264Cite as

Use Case Prediction Using Product Reviews Text Classification

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11871))

Abstract

Research into sentiment analysis and its capabilities at analysing product reviews has increased tremendously in recent years. In this paper, we propose an approach to classify product reviews and identify use cases. Several iterations showing the application of natural language processing techniques and machine learning classifications are depicted. A number of machine learning classifiers are trained/tested in various iterations, their performance and accuracy at predicting the existence of use cases in product reviews is evaluated.

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Author information

Authors and Affiliations

  1. Anglia Ruskin University, East Road, Cambridge, CB1 1PT, UK

    Tinashe Wamambo, Cristina Luca & Arooj Fatima

Authors
  1. Tinashe Wamambo
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  2. Cristina Luca
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  3. Arooj Fatima
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Corresponding author

Correspondence to Cristina Luca .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Cite this paper

Wamambo, T., Luca, C., Fatima, A. (2019). Use Case Prediction Using Product Reviews Text Classification. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-33607-3_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33606-6

  • Online ISBN: 978-3-030-33607-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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