Abstract
This paper presents a feature-driven opinion summarization method for customer reviews on the web based on identifying general features (characteristics) describing any product, product specific features and feature attributes (adjectives grading the characteristics). Feature attributes are assigned a polarity using on the one hand a previously annotated corpus and on the other hand by applying Support Vector Machines Sequential Minimal Optimization[1] machine learning with the Normalized Google Distance[2]. Reviews are statistically summarized around product features using the polarity of the feature attributes they are described by.
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References
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Balahur, A., Montoyo, A. (2008). Multilingual Feature-Driven Opinion Extraction and Summarization from Customer Reviews. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds) Natural Language and Information Systems. NLDB 2008. Lecture Notes in Computer Science, vol 5039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69858-6_39
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DOI: https://doi.org/10.1007/978-3-540-69858-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69857-9
Online ISBN: 978-3-540-69858-6
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