Review Selection Using Micro-Reviews

Thanh-Son Nguyen, Hady Wirawan Lauw, Panayiotis Tsaparas

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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Micro-reviews </i> 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 cover 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 cover 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.