AR-Miner: Mining Informative Reviews for Developers from Mobile App Marketplace

被引：320

作者：

Chen, Ning ^{[1
]}

Lin, Jialiu ^{[2
]}

Hoi, Steven C. H. ^{[1
]}

Xiao, Xiaokui ^{[1
]}

Zhang, Boshen ^{[1
]}

机构：

[1] Nanyang Technol Univ, Singapore, Singapore

[2] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

来源：

36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2014) | 2014年

关键词：

User feedback; mobile application; user reviews; data mining;

D O I：

10.1145/2568225.2568263

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：767 / 778

页数：12

共 36 条

[1]

Abulaish M, 2009, LECT NOTES COMPUT SC, V5909, P219, DOI 10.1007/978-3-642-11164-8_35

[2]

[Anonymous], 2009, Search Engines: Information Retrieval in Practice

[3]

[Anonymous], 2012, P 18 ACM SIGKDD INT, DOI DOI 10.1145/2339530.2339662

[4]

Antoniol G, 2008, P 2008 C CTR ADV STU, DOI [10.1145/1463788.1463819, DOI 10.1145/1463788.1463819]

[5]

Anvik J., 2006, P 28 INT C SOFTW ENG, P361, DOI [DOI 10.1145/1134285.1134336, 10.1145/1134285.1134336]

[6]

Bacchelli A, 2012, PROC INT CONF SOFTW, P375, DOI 10.1109/ICSE.2012.6227177

[7] Latent Dirichlet allocation [J].

Blei, DM ;

Ng, AY ;

Jordan, MI .

JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022

[8]

Bo P., 2008, Foundations and Trends in Information Retrieval, V2, P1, DOI DOI 10.1561/1500000011

[9]

Carreño LVG, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P582, DOI 10.1109/ICSE.2013.6606604

[10]

Caruana R, 2006, ICML 06: proceedings of the 23rd International Conference on Machine Learning, P161, DOI [DOI 10.1145/1143844.1143865, 10.1145/1143844.1143865.]

← 1 2 3 4 →