Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method

Rai Markus Panamuan; Debi  Handika; Muhamad Rizki  Pratama; Weiskhy Steven  Dharmawan; Lady Agustin  Fitriana; Riski Annisa

doi:10.59934/jaiea.v5i3.2364

Authors

Rai Markus Panamuan UNIVERSITAS BINA SARANA INFORMATIKA
Debi Handika Universitas Bina Sarana Informatika
Muhamad Rizki Pratama Universitas Bina Sarana Informatika
Weiskhy Steven Dharmawan Universitas Bina Sarana Informatika
Lady Agustin Fitriana Universitas Bina Sarana Informatika
Riski Annisa Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i3.2364

Keywords:

Clash of Clans, Latent Dirichlet Allocation, Natural Language Processing, Topic Modeling

Abstract

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.

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