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Context-Aware Sentiment Detection from Ratings

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Research and Development in Intelligent Systems XXXIII (SGAI 2016)

Abstract

The explosion of user-generated content, especially tweets, customer reviews, makes it possible to build sentiment lexicons automatically by harnessing the consistency between the content and its accompanying emotional signal, either explicitly or implicitly. In this work we describe novel techniques for automatically producing domain specific sentiment lexicons that are optimised for the language patterns and idioms of a given domain. We describe how we use review ratings as sentiment signals. We also describe an approach to recognising contextual variations in sentiment and show how these variations can be exploited in practice. We evaluate these ideas in a number of different product domains.

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Notes

  1. 1.

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

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Acknowledgments

This work is supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289.

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Authors and Affiliations

  1. Fudan University, Shanghai, China

    Yichao Lu

  2. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland

    Ruihai Dong & Barry Smyth

Authors
  1. Yichao Lu
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  2. Ruihai Dong
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  3. Barry Smyth
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Corresponding author

Correspondence to Ruihai Dong .

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Editors and Affiliations

  1. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Max Bramer

  2. School of Computing, Engineering and Mathematics, University of Brighton, Brighton, East Sussex, United Kingdom

    Miltos Petridis

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Lu, Y., Dong, R., Smyth, B. (2016). Context-Aware Sentiment Detection from Ratings. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-47175-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47174-7

  • Online ISBN: 978-3-319-47175-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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