NationalMood: Large-scale Estimation of People's Mood from Web Search Query and Mobile Sensor Data
This addresses the challenge of real-time mood estimation for web users to enable user-centric services, though it is incremental in combining existing data types.
The paper tackled the problem of estimating people's mood from web search queries and mobile sensor data, revealing that mood-based ad delivery can be effective and that their National Mood Score inversely correlated with COVID-19 case numbers and showed weekly rhythms.
The ability to estimate current affective statuses of web users has considerable potential towards the realization of user-centric opportune services. However, determining the type of data to be used for such estimation as well as collecting the ground truth of such affective statuses are difficult in the real world situation. We propose a novel way of such estimation based on a combinational use of user's web search queries and mobile sensor data. Our large-scale data analysis with about 11,000,000 users and 100 recent advertisement log revealed (1) the existence of certain class of advertisement to which mood-status-based delivery would be significantly effective, (2) that our "National Mood Score" shows the ups and downs of people's moods in COVID-19 pandemic that inversely correlated to the number of patients, as well as the weekly mood rhythm of people.