Abstract
The information in customer reviews is of great interest to both companies and consumers. This information is usually presented as non-structured free-text so that automatically extracting and rating user opinions about a product is a challenging task. Moreover, this opinion highly depends on the product features on which the user judgments and impressions are expressed. Following this idea, our goal is to predict the overall rating of a product review based on the user opinion about the different product features that are evaluated in the review. To this end, the system first identifies the features that are relevant to consumers when evaluating a certain type of product, as well as the relative importance or salience of such features. The system then extracts from the review the user opinions about the different product features and quantifies such opinions. The salience of the different product features and the values that quantify the user opinions about them are used to construct a Vector of Feature Intensities which represents the review and will be the input to a machine learning model that classifies the review into different rating categories. Our method is evaluated over 1000 hotel reviews from booking.com. The results compare favorably with those achieved by other systems addressing similar evaluations.
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de Albornoz, J.C., Plaza, L., Gervás, P., Díaz, A. (2011). A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_8
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DOI: https://doi.org/10.1007/978-3-642-20161-5_8
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