Prediction of Churning Game Users Based on Social Activity and Churn Graph Neural Networks

Han, You-Jung; Moon, Jihoon; Woo, Jiyoung

doi:10.1109/access.2024.3429559

IEEE Access

2024

DOI: 10.1109/access.2024.3429559

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Prediction of Churning Game Users Based on Social Activity and Churn Graph Neural Networks

You-Jung Han,

Jihoon Moon,

Jiyoung Woo

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2024

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Cited by 2 publications

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Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Przybyła-Kasperek,

Marfo,

Sulikowski

2024

Applied Sciences

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Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being “black-box” models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work.

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Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Przybyła-Kasperek,

Marfo,

Sulikowski

2024

Applied Sciences

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Differential Impacts of Environmental, Social, and Governance News Sentiment on Corporate Financial Performance in the Global Market: An Analysis of Dynamic Industries Using Advanced Natural Language Processing Models

Kim,

Kang,

Jeon

et al. 2024

Electronics

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This study examines how sentiment analysis of environmental, social, and governance (ESG) news affects the financial performance of companies in innovative sectors such as mobility, technology, and renewable energy. Using approximately 9828 general ESG articles from Google News and approximately 140,000 company-specific ESG articles, we performed term frequency-inverse document frequency (TF-IDF) analysis to identify key ESG-related terms and visualize their materiality across industries. We then applied models such as bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa), and big bidirectional encoder representations from transformers (BigBird) for multiclass sentiment analysis, and distilled BERT (DistilBERT), a lite BERT (ALBERT), tiny BERT (TinyBERT), and efficiently learning an encoder that classifies token replacements accurately (ELECTRA) for positive and negative sentiment identification. Sentiment analysis results were correlated with profitability, cash flow, and stability indicators over a three-year period (2019–2021). ESG ratings from Morgan Stanley Capital International (MSCI), a prominent provider that evaluates companies’ sustainability practices, further enriched our analysis. The results suggest that sentiment impacts financial performance differently across industries; for example, positive sentiment correlates with financial success in mobility and renewable energy, while consumer goods often show positive sentiment even with low environmental ESG scores. The study highlights the need for industry-specific ESG strategies, especially in dynamic sectors, and suggests future research directions to improve the accuracy of ESG sentiment analysis.

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Prediction of Churning Game Users Based on Social Activity and Churn Graph Neural Networks

Cited by 2 publications

References 36 publications

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Differential Impacts of Environmental, Social, and Governance News Sentiment on Corporate Financial Performance in the Global Market: An Analysis of Dynamic Industries Using Advanced Natural Language Processing Models

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