Acquisition of Knowledge with Time Information from Twitter

In this paper, we propose a knowledge acquisition method for non-task-oriented dialogue systems. Such dialogue systems need a wide variety of knowledge for generating appropriate and sophisticated responses. However, constructing such knowledge is costly. To solve this problem, we focus on a relation about each tweet and the posted time. First, we extract event words, such as verbs, from tweets. Second, we generate frequency distribution for five different time divisions: e.g., a monthly basis. Then, we remove burst words on the basis of variance for obtaining refined distributions. We checked high ranked words in each time division. As a result, we obtained not only common sense things such as “sleep” in night but also interesting activities such as “recruit” in April and May (April is the beginning of the recruitment process for the new year in Japan.) and “raise the spirits/plow into” around 9 AM for inspiring oneself at the beginning of his/her work of the day. In addition, the knowledge that our method extracts probably contributes to not only dialogue systems but also text mining and behavior analysis of data on social media and so on.