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.
翻译:估计网络用户目前的情感状态的能力对于实现社会以用户为中心的服务具有相当大的潜力。然而,在现实世界的网络服务中,很难确定用于这种估计的数据类型,以及收集这种情感状态的地面真相。我们提出一种基于综合使用用户网络搜索查询和移动感应数据进行这种估计的新方法。这个系统部署在我们的产品服务器堆中,对超过11,000,000名用户进行了大规模的数据分析。有趣的是,我们提议的“全Nation-lobal Mood评分 ” 将全国用户的情绪价值捆绑在一起,(1) 显示人们情绪的每日和每周的节奏,(2) 解释COVID-19大流行病中人们情绪的上升和下降,这与新的COVID-19案例的数量反常不同步,(3) 检测与大新闻的联系,这可能会同时影响许多用户的情绪状态,即使是在微细微的时间分辨率上,例如小时的顺序上。