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Inferring Aspect-Specific Opinion Structure in Product Reviews Using Co-training

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9042))

Abstract

Opinions expressed about a particular subject are often nuanced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating overall ratings. We contribute a new confidence-based co-training algorithm that can identify product aspects and sentiments expressed about such aspects. Our algorithm offers better precision than existing methods, and handles previously unseen language well. We show competitive results on a set of opinionated sentences about laptops and restaurants from a SemEval-2014 Task 4 challenge.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Dave Carter & Diana Inkpen

  2. National Research Council, Ottawa, Canada

    Dave Carter

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  1. Dave Carter
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  2. Diana Inkpen
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Correspondence to Dave Carter .

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Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Carter, D., Inkpen, D. (2015). Inferring Aspect-Specific Opinion Structure in Product Reviews Using Co-training. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_17

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  • DOI: https://doi.org/10.1007/978-3-319-18117-2_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18116-5

  • Online ISBN: 978-3-319-18117-2

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