Topic-Based Tweet Clustering for Public Figures Using Ant Clustering

The aspects of life on a public figure, discussed by the community, are often exploited by the news media as topic information to create an article that can attract the attention of the reader. Efficiently, the media only needs to pay attention to social media to get some of the information. The more information needed leads to a vast amount of data involved, so the process becomes hard. In this paper, a tweet clustering system to determine topics from many documents in the form of text through text mining method using an ant clustering (AC) technique. AC is one of swarm intelligence algorithms inspired by the behavior of the ant colony in sorting corpses. Evaluation of a small dataset of text documents shows that four topics are successfully concluded: economy, social, politics, and government. The developed AC-based tweet clustering system produces an average cluster quality of the Dunn Index up to 0,3455.