Topic Detection and Tracking Towards Determining Public Agenda Items

Abstract

It is a known fact that all of the events that people in the society are exposed to while continuing their lives have important effects on their quality of life. Events that have significant effects on a large part of the society are shared with the public through news texts. With a perspective that keeps up with the digital age, the problem of automatic detection and tracking of events in the news with natural language processing methods is discussed. An event-based news clustering approach is presented for data regimentation, which is necessary to extract meaningful information from news in the form of heaps in online environments. In this approach, it is aimed to increase clustering performance and speed by making use of named entities. Additionally, an event-based text clustering dataset was created by the researchers and brought to the literature. By using the B-cubed evaluation metric on this test dataset, which consists of 930 different event groups and has a total of 19,848 news, a solution to the event-based text clustering problem was provided with an F-score of over 85%.