Journal Article

An automated mobile app labeling framework based on primary motivations for smartphone use

Ayanso, Anteneh, Han, Mingshan, Zihayat, Morteza

2022

Overview

PurposeThis paper aims to propose an automated mobile app labeling framework based on a novel app classification scheme that is aligned with users’ primary motivations for using smartphones. The study addresses the gaps in incorporating the needs of users and other context information in app classification as well as recommendation systems.Design/methodology/approachBased on a corpus of mobile app descriptions collected from Google Play store, this study applies extensive text analytics and topic modeling procedures to profile mobile apps within the categories of the classification scheme. Sufficient number of representative and labeled app descriptions are then used to train a classifier using machine learning algorithms, such as rule-based, decision tree and artificial neural network.FindingsExperimental results of the classifiers show high accuracy in automatically labeling new apps based on their descriptions. The accuracy of the classification results suggests a feasible direction in facilitating app searching and retrieval in different Web-based usage environments.Research limitations/implicationsAs a common challenge in textual data projects, the problem of data size and data quality issues exists throughout the multiple phases of experiments. Future research will extend the data collection scope in many aspects to address the issues that constrained the current experiments.Practical implicationsThese empirical experiments demonstrate the feasibility of textual data analysis in profiling apps and user context information. This study also benefits app developers by improving app descriptions through a better understanding of user needs and context information. Finally, the classification framework can also guide practitioners in customizing products and services beyond mobile apps where context information and user needs play an important role.Social implicationsGiven the widespread usage and applications of smartphones today, the proposed app classification framework will have broader implications to different Web-based application environments.Originality/valueWhile there have been other classification approaches in the literature, to the best of the authors’ knowledge, this framework is the first study on building an automated app labeling framework based on primary motivations of smartphone usage.

• Publisher: Emerald Group Publishing Limited
• Subject: Accuracy / Algorithms / Application service providers / Applications programs / Artificial neural networks / Automation / Classification / Classification schemes / Classifiers / Context / Data analysis / Data collection / Data quality / Decision making / Decision trees / Descriptions / Empirical analysis / Experiments / Feasibility / Frame analysis / Information / Labeling / Language usage / Machine learning / Mobile computing / Motivation / Needs / Neural networks / Novels / Profiles / Recommender systems / Retrieval / Rules / Smartphones / Software / Usability / Use statistics / User behavior / User generated content / User needs