Analyzing Audience Engagement in Esports: Sentiment and LLM-Based Topic Insights from Live Chats in South Asia

This study investigates the dynamics of audience engagement in esports through sentiment analysis of live chat data and topic discovery, focusing on the popular game PUBG Mobile across Bangladesh, India, and Pakistan. A dataset encompassing nearly 15 million live chat messages and video metadata was utilized, employing a pre-trained RoBERTa model for sentiment classification, categorizing user sentiments into positive, negative, and neutral. The analysis revealed a significant increase in positive sentiment among Bangladeshi viewers, suggesting a shift towards a more favorable perception of esports. Additionally, the Gemma 7b-it large language model was applied to identify key discussion topics within the live chat, uncovering themes related to gameplay strategies and community interactions. The findings indicate a strong correlation between view counts and audience engagement, highlighting opportunities for advertisers to connect with dedicated esports fans. Despite limitations such as the focus on official YouTube channels and the resource constraints of sentiment analysis, this research offers valuable insights into real-time audience engagement in esports, paving the way for future studies to explore broader contexts and multimodal data integration.