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Mining Worse and Better Opinions. Unsupervised and Agnostic Aggregation of Online Reviews
lib:08a2b4fcc317dfa3 (v1.0.0)
Authors: Michela Fazzolari,Marinella Petrocchi,Alessandro Tommasi,Cesare Zavattari
ArXiv: 1704.05393
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1704.05393v1
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.
ArXiv: 1704.05393
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1704.05393v1
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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