SportsOpi: A Novel Dataset for Analyzing Public Sentiment on Controversial Sports Events in YouTube Comments

Sports engages billions of followers worldwide and impacts theeconomy. Sports controversies often ignite passionate discus-sions among fans, analysts, and players. With the rise of socialmedia, platforms like YouTube have become central to these discus-sions. This study aims to analyze the stances or perform opinionmining namely for, against, and neutral on comments from fa-mous social media platforms like YouTube for famous public sportscontroversies. To our knowledge, it is the first-ever study and dataset (hand curated) of civicengagement in controversial sports events spanning around 40 years.LLMs (Llama and Deepseek reasoning family) were used for initialannotations (stance) of comments and later fine-tuned for comparative performance analysis ( 30% boost in accuracy). This dataset presents a collection of YouTube comments (around 43k)  on famousand controversial Public Sports Events. We explore public sentiment analysis (stance detection) on a total of 6 famous controversial sports incidents by extracting and processing YouTube comments.Stance detection is performed on those events through fine-tuningof models like Llama-3.1-8b and Deepseek reasoning models (Llama-8b distilled) on comments from events like The Underarm Incident,Jonny Bairstow’s Run-Out Incident, Ashwin’s Mankading Event,Luis Suarez Handball Event etc.