Mining Comments and Sentiments in YouTube Live Chat Data

The proliferation of social media and internet usage has led to an exponential growth in data volume and dynamic user interactions. Within this digital landscape, YouTube Live Chat analysis has emerged as a compelling area for understanding spontaneous user engagement. Central to this analysis is sentiment analysis, which extracts opinions and sentiments, offering valuable insights for content creators and industries seeking to understand user perspectives. This paper presents a thorough examination of mining comments and sentiments within the dynamic environment of YouTube Live Chat data. Our study involves analyzing the live chat of YouTube live streams, applying sentiment analysis to the live feed, and presenting a real-time dashboard to streamers. By leveraging sentiment analysis, including the interpretation of emojis, we aim to offer content creators and platform administrators valuable insights into audience reactions, opinions, and sentiments. Our system enables users to access real-time sentiment analysis of live chat interactions, facilitating the delivery of more engaging content. Ultimately, our goal is to equip content creators, platform administrators, and researchers with actionable insights into audience engagement, preferences, and emotional resonance within the digital realm.