Improving Music Playback Prediction via Singer Popularity and Aspect-based Sentiment Analysis from Social Network

抄録

For online music streaming platforms, social network analysis has provided extra information for hit song prediction as social networks become a new channel for the public to express their opinions toward all possible topics. This research exploits social network analysis for hit song prediction via singer popularity and aspect-based sentiment analysis. For each song, we analyze the popularity of the singers and songs on the social network ”PTT”, and apply the aspect-based sentiment analysis (ABSA) to perform sentiment analysis on the singer. These results are combined with platform information to predict the playbacks of popular songs. Experimental results show that adding "singer's popularity" and "target emotion" can reduce the RMSE (Root Mean Square Error) of subsequent on-demand songs.