Research Concerning Topic Extraction Method Using Burst Levels in Microblogs

Abstract

With the development of communication devices including smartphones, utilization of SNS (Social Networking Services) such as microblogs is becoming more active. Microblogs contain posts about actual events such as news, and many methods for analyzing topics from these posts have been proposed. However, for approaches with a focus on posted content, when targeting microblogs in which new posts are added over time, the difficulty of structuring models that comprise topics is an issue. Therefore, this study develops a method of obtaining topics from the burst levels of multiple keywords with a focus on changes in accordance with time series in burst levels of keywords appearing in posted contents. In demonstration experiments, we conducted comparative experiments to compare existing topic extraction methods with our proposed method, and we verified that it is possible for our proposed method to detect topics that existing methods are unable to obtain.