  The ability to estimate the current affective statuses of web users has
considerable potential for the realization of user-centric services in the
society. However, in real-world web services, it is difficult to determine the
type of data to be used for such estimation, as well as collecting the ground
truths of such affective statuses. We propose a novel method of such estimation
based on the combined use of user web search queries and mobile sensor data.
The system was deployed in our product server stack, and a large-scale data
analysis with more than 11,000,000 users was conducted. Interestingly, our
proposed "Nation-wide Mood Score," which bundles the mood values of users
across the country, (1) shows the daily and weekly rhythm of people's moods,
(2) explains the ups and downs of people's moods in the COVID-19 pandemic,
which is inversely synchronized to the number of new COVID-19 cases, and (3)
detects the linkage with big news, which may affect many user's mood states
simultaneously, even in a fine-grained time resolution, such as the order of
hours.
