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2017 | OriginalPaper | Buchkapitel

Mining Worse and Better Opinions

Unsupervised and Agnostic Aggregation of Online Reviews

verfasst von : Michela Fazzolari, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari

Erschienen in: Web Engineering

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a novel approach for aggregating online reviews, according to the opinions they express. Our methodology is unsupervised, due to the fact that it does not rely on pre-labeled reviews, and it is agnostic, since it does not make any assumption about the domain or the language of the review content. We measure the adherence of a review content to the domain terminology extracted from a review set. First, we demonstrate the informativeness of the adherence metric with respect to the score associated with a review. Then, we exploit the metric values to group reviews, according to the opinions they express. Our experimental campaign has been carried out on two large datasets collected from Booking and Amazon, respectively.

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Titel: Mining Worse and Better Opinions
verfasst von: Michela Fazzolari
Marinella Petrocchi
Alessandro Tommasi
Cesare Zavattari
Verlag: Springer International Publishing
Buch: Web Engineering
Print ISBN: 978-3-319-60130-4

Electronic ISBN: 978-3-319-60131-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-60131-1_35

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