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Good Location, Terrible Food: Detecting Feature Sentiment in User-Generated Reviews

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 usergenerated 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 usergenerated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.
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Submitted on : Wednesday, June 5, 2013 - 12:34:39 PM
Last modification on : Wednesday, April 8, 2020 - 3:35:50 PM
Long-term archiving on: : Friday, September 6, 2013 - 4:11:58 AM


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  • HAL Id : hal-00830560, version 1



Mario Cataldi, Ballatore Andrea, Tiddi Ilaria, Marie-Aude Aufaure. Good Location, Terrible Food: Detecting Feature Sentiment in User-Generated Reviews. International Journal of Social Network Analysis and Mining (SNAM), 2013, pp.1-16. ⟨hal-00830560⟩



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