SMU Data Science Review


Twitch.tv streamers have a rare opportunity to receive immediate feedback from their audience through a real-time chat log that is rife with sentiment information. Tools that can help a streamer understand how they need to influence their audience can be useful in increasing the donations and subscriptions they earn. Although millions around the world stream on Twitch, only a minuscule fraction of these streamers earn a living streaming alone. This paper aimed to provide muchneeded guidance to enable more streamers to succeed. We used stream logs, known as VODs (video on demand), which can be easily accessed through Twitch’s API or web interface, and parsed these logs for chat and donation data. After normalizing the data, we performed sentiment analysis using a combination of VADER, TextBlob, and Flair algorithms. We found that chat sentiment is a useful indicator for predicting the occurrence of donations. The results have set the foundation future researchers and developers can use to create tools and further our collective understanding of stream viewer sentiment.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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