AR-Miner: Mining informative reviews for developers from mobile app marketplace

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer fro...

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Main Authors: CHEN, Ning, LIN, Jialiu, HOI, Steven C. H., XIAO, Xiaokui, ZHANG, Boshen
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2323
https://ink.library.smu.edu.sg/context/sis_research/article/3323/viewcontent/AR_Miner_Mining_Informative_Reviews_for_Developers_from_Mobile_App_Marketplace.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-33232020-03-31T06:12:36Z AR-Miner: Mining informative reviews for developers from mobile app marketplace CHEN, Ning LIN, Jialiu HOI, Steven C. H. XIAO, Xiaokui ZHANG, Boshen With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers. 2014-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2323 info:doi/10.1145/2568225.2568263 https://ink.library.smu.edu.sg/context/sis_research/article/3323/viewcontent/AR_Miner_Mining_Informative_Reviews_for_Developers_from_Mobile_App_Marketplace.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 user feedback mobile application user reviews data mining Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic user feedback
mobile application
user reviews
data mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle user feedback
mobile application
user reviews
data mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
CHEN, Ning
LIN, Jialiu
HOI, Steven C. H.
XIAO, Xiaokui
ZHANG, Boshen
AR-Miner: Mining informative reviews for developers from mobile app marketplace
description With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.
format text
author CHEN, Ning
LIN, Jialiu
HOI, Steven C. H.
XIAO, Xiaokui
ZHANG, Boshen
author_facet CHEN, Ning
LIN, Jialiu
HOI, Steven C. H.
XIAO, Xiaokui
ZHANG, Boshen
author_sort CHEN, Ning
title AR-Miner: Mining informative reviews for developers from mobile app marketplace
title_short AR-Miner: Mining informative reviews for developers from mobile app marketplace
title_full AR-Miner: Mining informative reviews for developers from mobile app marketplace
title_fullStr AR-Miner: Mining informative reviews for developers from mobile app marketplace
title_full_unstemmed AR-Miner: Mining informative reviews for developers from mobile app marketplace
title_sort ar-miner: mining informative reviews for developers from mobile app marketplace
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2323
https://ink.library.smu.edu.sg/context/sis_research/article/3323/viewcontent/AR_Miner_Mining_Informative_Reviews_for_Developers_from_Mobile_App_Marketplace.pdf
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