Social Media Dynamics: Twitter Users’ Responses to the Presence of Naturalized Players in Indonesia's National Football Team

Abstract

Public discourse on the naturalization of players in Indonesia’s national football team continues to grow, with social media platforms such as Twitter becoming primary outlets for expressing opinions. This study employs advanced sentiment analysis techniques, utilizing IndoBERT—a deep learning model specifically designed for the Indonesian language—to analyze the sentiments of Twitter users. The analytical process includes data preprocessing, sentiment distribution visualization, and model performance evaluation. Findings reveal that IndoBERT captures public sentiments more comprehensively and accurately than conventional sentiment analysis models. Sentiment polarity is significantly influenced by factors such as player performance, public expectations, and media narratives. This study offers practical insights for policymakers in Indonesian football to support data-driven strategic decision-making. Furthermore, it underscores the value of natural language processing (NLP) and sentiment analysis in understanding complex socio-cultural dynamics in the digital era.