論文誌 (国際) Large-scale Estimation and Analysis of Web Users' Mood from Web Search Query and Mobile Sensor Data
Wataru Sasaki (Keio University), Satoki Hamanaka (Keio University), Satoko Miyahara, Kota Tsubouchi, Jin Nakazawa (Keio University), Tadashi Okoshi (Keio University)
Big Data (Big Data)
The ability to estimate the current affective statuses of web users has considerable potential for the realization of user-centric opportune services. 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.