research-article

Mining User Reviews for Mobile App Comparisons

Authors:

Xiangqun ChenAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 1, Issue 3

Article No.: 75, Pages 1 - 15

https://doi.org/10.1145/3130935

Published: 11 September 2017 Publication History

Abstract

As the number of mobile apps keeps increasing, users often need to compare many apps, in order to choose one that best fits their needs. Fortunately, as there are so many users sharing an app market, it is likely that some other users with the same preferences have already made the comparisons and shared their opinions. For example, a user may state that an app is better in power consumption than another app in a review, then the review would help other users who care about battery life while choosing apps. This paper presents a method to identify comparative reviews for mobile apps from an app market, which can be used to provide fine-grained app comparisons based on different topics. According to experiments on 5 million reviews from Google Play and manual assessments on 900 reviews, our method is able to identify opinions accurately and provide meaningful comparisons between apps, which could in turn help users find desired apps based on their preferences.

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Cited By

Arambepola NMunasinghe LWarnajith N(2024)Factors Influencing Mobile App User Experience: An Analysis of Education App User Reviews2024 4th International Conference on Advanced Research in Computing (ICARC)10.1109/ICARC61713.2024.10499727(223-228)Online publication date: 21-Feb-2024
https://doi.org/10.1109/ICARC61713.2024.10499727
Sharma NAli AKabir M(2024)A review of sentiment analysis: tasks, applications, and deep learning techniquesInternational Journal of Data Science and Analytics10.1007/s41060-024-00594-xOnline publication date: 1-Jul-2024
https://doi.org/10.1007/s41060-024-00594-x
Alturayeif NAljamaan HHassine J(2023)An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learningAutomated Software Engineering10.1007/s10515-023-00397-730:2Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1007/s10515-023-00397-7
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Mining User Reviews for Mobile App Comparisons

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 1, Issue 3

September 2017

2023 pages

EISSN:2474-9567

DOI:10.1145/3139486

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Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 February 2017

Published in IMWUT Volume 1, Issue 3

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Cited By

Arambepola NMunasinghe LWarnajith N(2024)Factors Influencing Mobile App User Experience: An Analysis of Education App User Reviews2024 4th International Conference on Advanced Research in Computing (ICARC)10.1109/ICARC61713.2024.10499727(223-228)Online publication date: 21-Feb-2024
https://doi.org/10.1109/ICARC61713.2024.10499727
Sharma NAli AKabir M(2024)A review of sentiment analysis: tasks, applications, and deep learning techniquesInternational Journal of Data Science and Analytics10.1007/s41060-024-00594-xOnline publication date: 1-Jul-2024
https://doi.org/10.1007/s41060-024-00594-x
Alturayeif NAljamaan HHassine J(2023)An automated approach to aspect-based sentiment analysis of apps reviews using machine and deep learningAutomated Software Engineering10.1007/s10515-023-00397-730:2Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1007/s10515-023-00397-7
Zhou WWang YGao CYang F(2022)Emerging topic identification from app reviews via adaptive online biterm topic modeling基于自适应在线双词主题模型的应用程序评论新兴主题识别Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.210046523:5(678-691)Online publication date: 11-Apr-2022
https://doi.org/10.1631/FITEE.2100465
Xiao JLi HWu MJin HDeen MCao J(2022)A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open IssuesACM Computing Surveys10.1145/353068255:5(1-35)Online publication date: 3-Dec-2022
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Wang HChen CXing ZGrundy J(2022)DiffTech: Differencing Similar Technologies From Crowd-Scale Comparison DiscussionsIEEE Transactions on Software Engineering10.1109/TSE.2021.305988548:7(2399-2416)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSE.2021.3059885
Hu YWang HJi TXiao XLuo XGao PGuo Y(2021)CHAMPProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00089(933-945)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00089
Alturaief NAljamaan HBaslyman M(2021)AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)10.1109/ASEW52652.2021.00049(211-218)Online publication date: Nov-2021
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Al-Hawari ANajadat HShatnawi R(2020)Classification of application reviews into software maintenance tasks using data mining techniquesSoftware Quality Journal10.1007/s11219-020-09529-8Online publication date: 28-Aug-2020
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Nguyen VRupavatharam SLiu LHoward RGruteser MGanti RJiang XPicco GZhou X(2019)HandSenseProceedings of the 17th Conference on Embedded Networked Sensor Systems10.1145/3356250.3360040(285-297)Online publication date: 10-Nov-2019
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