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December 2018 Single stage prediction with embedded topic modeling of online reviews for mobile app management
Shawn Mankad, Shengli Hu, Anandasivam Gopal
Ann. Appl. Stat. 12(4): 2279-2311 (December 2018). DOI: 10.1214/18-AOAS1152

Abstract

Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app. We create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship between term frequency and a given response variable in addition to co-occurrences between terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization is combined with ordinal regression to provide guidance from online reviews on a single app’s performance as well as systematically compare different apps over time for benchmarking of features and consumer sentiment. We apply our approach using a dataset of over 100,000 mobile reviews over several years for three of the most popular online travel agent apps from the iTunes and Google Play marketplaces.

Citation

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Shawn Mankad. Shengli Hu. Anandasivam Gopal. "Single stage prediction with embedded topic modeling of online reviews for mobile app management." Ann. Appl. Stat. 12 (4) 2279 - 2311, December 2018. https://doi.org/10.1214/18-AOAS1152

Information

Received: 1 July 2016; Revised: 1 February 2018; Published: December 2018
First available in Project Euclid: 13 November 2018

zbMATH: 07029455
MathSciNet: MR3875701
Digital Object Identifier: 10.1214/18-AOAS1152

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 4 • December 2018
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