Review Selection Using Micro-Reviews
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-rev...
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sg-smu-ink.sis_research-33122019-06-06T02:43:18Z Review Selection Using Micro-Reviews NGUYEN, Thanh-Son LAUW, Hady W. TSAPARAS, Panayiotis 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. 2015-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2312 info:doi/10.1109/TKDE.2014.2356456 https://ink.library.smu.edu.sg/context/sis_research/article/3312/viewcontent/tkde15.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Micro-review review selection coverage social media Databases and Information Systems Numerical Analysis and Scientific Computing Social Media |
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Micro-review review selection coverage social media Databases and Information Systems Numerical Analysis and Scientific Computing Social Media NGUYEN, Thanh-Son LAUW, Hady W. TSAPARAS, Panayiotis Review Selection Using Micro-Reviews |
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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. |
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NGUYEN, Thanh-Son LAUW, Hady W. TSAPARAS, Panayiotis |
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NGUYEN, Thanh-Son LAUW, Hady W. TSAPARAS, Panayiotis |
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NGUYEN, Thanh-Son |
title |
Review Selection Using Micro-Reviews |
title_short |
Review Selection Using Micro-Reviews |
title_full |
Review Selection Using Micro-Reviews |
title_fullStr |
Review Selection Using Micro-Reviews |
title_full_unstemmed |
Review Selection Using Micro-Reviews |
title_sort |
review selection using micro-reviews |
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Institutional Knowledge at Singapore Management University |
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2015 |
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https://ink.library.smu.edu.sg/sis_research/2312 https://ink.library.smu.edu.sg/context/sis_research/article/3312/viewcontent/tkde15.pdf |
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