  Collective emotion has been traditionally evaluated by questionnaire survey
on a limited number of people. Recently, big data of written texts on the
Internet has been available for analyzing collective emotion for very large
scales. Although short-term reflection between collective emotion and real
social phenomena has been widely studied, long-term dynamics of collective
emotion has not been studied so far due to the lack of long persistent data
sets. In this study, we extracted collective emotion over a 10-year period from
3.6 billion Japanese blog articles. Firstly, we find that collective emotion
shows clear periodic cycles, i.e., weekly and seasonal behaviors, accompanied
with pulses caused by natural disasters. For example, April is represented by
high Tension, probably due to starting school in Japan. We also identified
long-term memory in the collective emotion that is characterized by the
power-law decay of the autocorrelation function over several months.
