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Sentiment Analysis and Ontology Engineering pp 379–397Cite as

Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 639))

Abstract

Consumers often provide on-line reviews on products or services they have purchased, and frequently seek on-line reviews about a product or service before deciding whether to make a purchase. Organisations seek consumer opinions about their products, since this invaluable information allows them to improve future product versions, and to predict sales. The vast amount of on-line customer reviews has attracted research into approaches for intelligently mining these reviews to support decision-making processes. This chapter provides an overview of recent fuzzy-based approaches to sentiment analysis of customer reviews. It also presents a framework which can be utilised for sentiment analysis and review rating prediction tasks. The framework includes methods for preparing the dataset; extracting the best features for prediction via Singular Value Decomposition and a Genetic Algorithm; and constructing a classifier for performing the review rating predictions.

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Notes

  1. 1.

    Available at http://snap.stanford.edu/data/web-Amazon.html.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Georgina Cosma & Giovanni Acampora

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  1. Georgina Cosma
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  2. Giovanni Acampora
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Correspondence to Giovanni Acampora .

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Editors and Affiliations

  1. Department of Electrical and Computer, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. and Technology, National Taiwan University of Scien, Taipei, Taiwan

    Shyi-Ming Chen

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Cosma, G., Acampora, G. (2016). Neuro-Fuzzy Sentiment Analysis for Customer Review Rating Prediction. In: Pedrycz, W., Chen, SM. (eds) Sentiment Analysis and Ontology Engineering. Studies in Computational Intelligence, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-30319-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-30319-2_15

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