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

Cataldi, Mario; Ballatore, Andrea; Tiddi, Ilaria and Aufaure, Marie-Aude (2013). Good location, terrible food: detecting feature sentiment in user-generated reviews. Social Network Analysis and Mining, 3(4) pp. 1149–1163.

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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.

Item Type: Journal Item
Copyright Holders: 2013 Springer-Verlag
ISSN: 1869-5469
Keywords: sentiment analysis; opinion mining; natural language processing; feature detection; dependency graphs
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 40060
Depositing User: Kay Dave
Date Deposited: 01 May 2014 12:25
Last Modified: 16 Feb 2019 02:16
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