Hyeonho Song  Kunwoo Park  Meeyoung Cha

10.5626/JOK.2020.47.12.1153

Live Streaming emote Word Embedding Popularity Prediction

This study presents an embedding method that effectively learns emote’s meaning in Twitch.tv to understand the audience reaction in live streaming. The proposed method first trains an embedding matrix for text and emotes, respectively, and merges the two matrices into one. Using 2,220,761 clips shared on Twitch.tv, this study conducted two experiments: clustering and clip popularity prediction. Results showed that the approach identifies emote clusters that express a similar emotion and detects popular clips. Future studies could utilize the proposed emote embedding method for the highlight prediction of a live stream.