Playlist generation based on user perception of songs

Large online music collections often frustrate users and have increased the importance of recommender systems. This has led to interesting problem of automated playlist generation. Most of the existing playlist's compare a pair songs based on low-level/mid-level features and calculate the similarity. These systems lack user perception of music. This work supplements such existing systems by providing user perception of songs conveyed in Twitter messages. The proposed system combines audio based features and sentiment associated with the song. This unique fusion not only yields better results but also better user satisfaction. Further a validation on 200 users who used our playlist showed that atleast 67% of the songs in the playlist were liked by the user.