With the increasing stress and unhealthy lifestyles in people's daily life, mental health problems are becoming a global concern. In particular, mood related mental health problems, such as mood disorders, depressions, and elation, are seriously impacting people's quality of life. However, due to the complexity and unstableness of personal mood, assessing and analyzing daily mood is both difficult and inconvenient, which is a major challenge in mental health care. In this paper, we propose a novel framework called Mood Miner for assessing and analyzing mood in daily life. Mood Miner uses mobile phone data -- mobile phone sensor data and communication data (including acceleration, light, ambient sound, location, call log, etc.) -- to extract human behavior pattern and assess daily mood. Our approach overcomes the problem of subjectivity and inconsistency of traditional mood assessment methods, and achieves a fairly good accuracy (around 50%) with minimal user intervention. We have built a system with clients on Android platform and an assessment model based on factor graph. We have also carried out experiments to evaluate our design in effectiveness and efficiency.
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