  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.
