Analyzing User Behavior in Mobile Game Applications Using Deep Data Analysis Approach

Rana Muhammad Amir Latif; Farhan Ullah; Leonardo Mostarda; Diletta Cacciagrano; Jiaqi Yin; Yue Zhao

doi:10.4018/979-8-3373-5062-2.ch005

Analyzing User Behavior in Mobile Game Applications Using Deep Data Analysis Approach

Rana Muhammad Amir Latif
, Farhan Ullah
, Leonardo Mostarda
, Diletta Cacciagrano
, Jiaqi Yin
, Yue Zhao

School of Software

East China Normal University
Prince Mohammad Bin Fahd University
University of Perugia
University of Camerino
Wenzhou-Kean University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

User reviews on platforms like Google Play Store shape mobile game demand through digital word-of-mouth (eWOM), directly influencing user preferences. This study applies Latent Semantic Analysis (LSA) to survey feedback, uncovering key drivers of satisfaction and dissatisfaction across mini-games, large area games, and enterprise applications. Positive sentiment is linked to entertainment value, ease of use, and appealing design, while negative feedback centers on bugs, poor gameplay mechanics, and intrusive ads. The analysis reveals both psychological and functional factors shaping user attitudes. Findings offer actionable insights for developers to enhance design, address common frustrations, and foster long-term engagement. These insights serve as a strategic guide for improving user experience and remaining competitive in the mobile gaming market.

Original language	English
Title of host publication	Computational and Deep Learning Models for Advanced Behavioral Analysis
Publisher	IGI Global
Pages	153-180
Number of pages	28
ISBN (Electronic)	9798337350646
ISBN (Print)	9798337350622
DOIs	https://doi.org/10.4018/979-8-3373-5062-2.ch005
State	Published - 1 Jan 2026

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Cite this

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abstract = "User reviews on platforms like Google Play Store shape mobile game demand through digital word-of-mouth (eWOM), directly influencing user preferences. This study applies Latent Semantic Analysis (LSA) to survey feedback, uncovering key drivers of satisfaction and dissatisfaction across mini-games, large area games, and enterprise applications. Positive sentiment is linked to entertainment value, ease of use, and appealing design, while negative feedback centers on bugs, poor gameplay mechanics, and intrusive ads. The analysis reveals both psychological and functional factors shaping user attitudes. Findings offer actionable insights for developers to enhance design, address common frustrations, and foster long-term engagement. These insights serve as a strategic guide for improving user experience and remaining competitive in the mobile gaming market.",

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Analyzing User Behavior in Mobile Game Applications Using Deep Data Analysis Approach

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