Leveraging Insights from Mobile App Reviews to Support Release Planning and Maintenance

Public Deposited
Resource Type
  • Developers seek to know what the users of the mobile apps they developed are saying about their products and what improvements they want. This helps developers update their apps according to the users' feedback and need. The goal of this thesis is to automate the process of leveraging key insights from the users' reviews to help developers better understand key concerns of large user population and plan future releases of their apps. In this thesis, we present an approach that automates the process of classifying user reviews into five categories according to the information contained within them. This information can range from improvements about the app to reporting bugs along with how users like/dislike the app. We then group related reviews into clusters by utilizing the LDA-based topic modelling and vector-space model. Then, for each generated cluster we extract the overall sentiment, generate hot topics and a short summary.

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Rights Notes
  • Copyright © 2018 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
Date Created
  • 2018


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