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Social Network Analysis and Mining

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01.12.2013 | Original Article

Good location, terrible food: detecting feature sentiment in user-generated reviews

verfasst von: Mario Cataldi, Andrea Ballatore, Ilaria Tiddi, Marie-Aude Aufaure

Erschienen in: Social Network Analysis and Mining | Ausgabe 4/2013

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Abstract

A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of “what people think” at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users’ opinions about specific features of products and services from user-generated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of user-generated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.

Vorheriger Artikel A learning method for Top-N recommendations with incomplete data

Nächster Artikel Network text analysis of conceptual overlap in interviews, newspaper articles and keywords

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This step is performed with the jExSLI tool, available at http://hlt.fbk.eu/en/technology/jExSLI.

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Titel: Good location, terrible food: detecting feature sentiment in user-generated reviews
verfasst von: Mario Cataldi
Andrea Ballatore
Ilaria Tiddi
Marie-Aude Aufaure
Publikationsdatum: 01.12.2013
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 4/2013
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-013-0119-7

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