Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T)

User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is inherently challenging. To address this problem, we propose MARK, a keyword-based framework for semi-automated review analysis. MARK allows an analyst describing his interests in one or some mobile apps by a set of keywords. It then finds and lists the reviews most relevant to those keywords for further analysis. It can also draw the trends over time of those keywords and detect their sudden changes, which might indicate the occurrences of serious issues. To help analysts describe their interests more effectively, MARK can automatically extract keywords from raw reviews and rank them by their associations with negative reviews. In addition, based on a vector-based semantic representation of keywords, MARK can divide a large set of keywords into more cohesive subsets, or suggest keywords similar to the selected ones.

[1]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[2]  Walid Maalej,et al.  User feedback in the appstore: An empirical study , 2013, 2013 21st IEEE International Requirements Engineering Conference (RE).

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  Raymond H. Myers,et al.  Probability and Statistics for Engineers and Scientists. , 1973 .

[5]  Yuanyuan Zhang,et al.  The App Sampling Problem for App Store Mining , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.

[6]  Rajesh Vasa,et al.  An Analysis of the Mobile App Review Landscape: Trends and Implications , 2013 .

[7]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[8]  Vysoké Učení,et al.  Statistical Language Models Based on Neural Networks , 2012 .

[9]  Ning Chen,et al.  AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.

[10]  G. Oehlert.  A first course in design and analysis of experiments , 2000 .

[11]  Gabriele Bavota,et al.  The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps , 2015, IEEE Transactions on Software Engineering.

[12]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[13]  Rishi Chandy,et al.  Identifying spam in the iOS app store , 2012, WebQuality '12.

[14]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[15]  ThelwallMike,et al.  Sentiment strength detection in short informal text , 2010 .

[16]  Walid Maalej,et al.  How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews , 2014, 2014 IEEE 22nd International Requirements Engineering Conference (RE).

[17]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[18]  Kristina Winbladh,et al.  Analysis of user comments: An approach for software requirements evolution , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[19]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[20]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[21]  Rachel Harrison,et al.  Retrieving and analyzing mobile apps feature requests from online reviews , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[22]  Kon Mouzakis,et al.  A preliminary analysis of mobile app user reviews , 2012, OZCHI.

[23]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[24]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[25]  Romain Rouvoy,et al.  A Recommender System of Buggy App Checkers for App Store Moderators , 2015, 2015 2nd ACM International Conference on Mobile Software Engineering and Systems.

[26]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[27]  A. Hassan,et al.  What Do Mobile App Users Complain About ? A Study on Free iOS Apps , 2014 .

[28]  Rachel Harrison,et al.  What are you complaining about?: a study of online reviews of mobile applications , 2013, BCS HCI.

[29]  Christos Faloutsos,et al.  Why people hate your app: making sense of user feedback in a mobile app store , 2013, KDD.