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Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Mining Worse and Better Opinions

Unsupervised and Agnostic Aggregation of Online Reviews

Authors : Michela Fazzolari, Marinella Petrocchi, Alessandro Tommasi, Cesare Zavattari

Published in: Web Engineering

Publisher: 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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Footnotes
1
Contiguous sequence of n words: “president of the USA” is a 4-gram.
 
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Metadata
Title
Mining Worse and Better Opinions
Authors
Michela Fazzolari
Marinella Petrocchi
Alessandro Tommasi
Cesare Zavattari
Copyright Year
2017
Book
Web Engineering

Print ISBN: 978-3-319-60130-4

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

Copyright Year: 2017

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

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