AI-Driven Cultural Sensitivity Analysis for Game Localization: A Case Study of Player Feedback in East Asian Markets

Abstract

This research presents an innovative AI-driven framework for analyzing cultural sensitivity in game localization across East Asian markets. Through the analysis of 1.2 million player feedback entries collected from China, Japan, and South Korea between 2022 and 2023, the study implements a sophisticated multi-layer neural network architecture incorporating both convolutional and recurrent components. The research develops comprehensive cultural sensitivity metrics encompassing narrative elements, visual design, gameplay mechanics, and social features. The AI model demonstrates high accuracy in cultural element identification, achieving cross-validation scores of 0.85-0.91 across different markets. The findings reveal distinct patterns in cultural preferences and adaptation requirements specific to each market, with correlation coefficients ranging from 0.65 to 0.92 for various cultural elements. The study identifies critical success factors in cultural adaptation strategies, including market-specific optimization approaches and cross-cultural compatibility considerations. The research contributes to both theoretical understanding and practical applications in game localization by establishing a data-driven framework for cultural sensitivity analysis. The findings provide valuable insights for developing targeted localization strategies in East Asian gaming markets while demonstrating the effectiveness of AI-driven approaches in cultural adaptation processes.