LJ2M dataset: Toward better understanding of music listening behavior and user mood

Recent years have witnessed a growing interest in modeling user behaviors in multimedia research, emphasizing the need to consider human factors such as preference, activity, and emotion in system development and evaluation. Following this research line, we present in this paper the LiveJournal two-million post (LJ2M) dataset to foster research on user-centered music information retrieval. The new dataset is characterized by the great diversity of real-life listening contexts where people and music interact. It contains blog articles from the social blogging website LiveJournal, along with tags self-reporting a user's emotional state while posting and the musical track that the user considered as the best match for the post. More importantly, the data are contributed by users spontaneously in their daily lives, instead of being collected in a controlled environment. Therefore, it offers new opportunities to understand the interrelationship among the personal, situational, and musical factors of music listening. As an example application, we present research investigating the interaction between the affective context of the listener and the affective content of music, using audio-based music emotion recognition techniques and a psycholinguistic tool. The study offers insights into the role of music in mood regulation and demonstrates how LJ2M can contribute to studies on real-world music listening behavior.