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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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
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