MoodScope: Building a mood sensor from smartphone usage patterns

We report a first-of-its-kind smartphone software system,
MoodScope, which infers the mood of its user based on how the
smartphone is used. Compared to smartphone sensors that measure
acceleration, light, and other physical properties, MoodScope is a
“sensor” that measures the mental state of the user and provides
mood as an important input to context-aware computing.
We run a formative statistical mood study with smartphonelogged
data collected from 32 participants over two months.
Through the study, we find that by analyzing communication history
and application usage patterns, we can statistically infer a user’s
daily mood average with an initial accuracy of 66%, which gradually
improves to an accuracy of 93% after a two-month personalized
training period. Motivated by these results, we build a service,
MoodScope, which analyzes usage history to act as a sensor of the
user’s mood. We provide a MoodScope API for developers to use
our system to create mood-enabled applications. We further create
and deploy a mood-sharing social application.