Topic Analysis Using a Finite Mixture Model

Hang Li, Kenji Yamanishi

Abstract: We address the issue of 'topic analysis,' by which is determined a text's topic structure, which indicates what topics are included in a text, and how topics change within the text. We propose a novel approach to this issue, one based on statistical modeling and learning. We represent topics by means of word clusters, and employ a finite mixture model to represent a word distribution within a text. Our experimental results indicate that our method significantly outperforms a method that combines existing techniques.