Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2015

Abstract

Given the proliferation of review content, and the fact that reviews are highly diverse and often unnecessarily verbose, users frequently face the problem of selecting the appropriate reviews to consume. Micro-reviews are emerging as a new type of online review content in the social media. Micro-reviews are posted by users of check-in services such as Foursquare. They are concise (up to 200 characters long) and highly focused, in contrast to the comprehensive and verbose reviews. In this paper, we propose a novel mining problem, which brings together these two disparate sources of review content. Specifically, we use coverage of micro-reviews as an objective for selecting a set of reviews that covers efficiently the salient aspects of an entity. Our approach consists of a two-step process: matching review sentences to micro-reviews, and selecting a small set of reviews that covers as many micro-reviews as possible, with few sentences. We formulate this objective as a combinatorial optimization problem, and show how to derive an optimal solution using Integer Linear Programming. We also propose an efficient heuristic algorithm that approximates the optimal solution. Finally, we perform a detailed evaluation of all the steps of our methodology using data collected from Foursquare and Yelp.

Keywords

Micro-review, review selection, coverage, social media

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

27

Issue

4

First Page

1098

Last Page

1111

ISSN

1041-4347

Identifier

10.1109/TKDE.2014.2356456

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2014.2356456

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