MoodScope

TLDR

MoodScope measures users’ mental state via smartphone usage, offering mood data for context‑aware computing, unlike conventional sensors that capture physical properties. The authors aim to develop MoodScope, a smartphone‑based system that infers users’ moods from their device usage. Using data logged from 32 participants over two months, the authors built MoodScope, providing an API for developers and a mood‑sharing app to collect and analyze usage patterns. The study shows that MoodScope can infer daily mood averages with 66 % accuracy initially, improving to 93 % after two months of personalized training.

Abstract

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 smartphone-logged 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 gradu-ally improves to an accuracy of 93% after a two-month personal-ized 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.