Topic modeling of research fields: An interdisciplinary perspective

This paper addresses the problem of scientific research analysis. We use the topic model Latent Dirichlet Allocation [2] and a novel classifier to classify research papers based on topic and language. Moreover, we show various insightful statistics and correlations within and across three research fields: Linguistics, Computational Linguistics, and Education. In particular, we show how topics change over time within each field, what relations and influences exist between topics within and across fields, as well as what trends can be established for some of the world's natural languages. Finally, we talk about trend prediction and topic suggestion as future extensions of this research.