Topic Analysis of Climate-Change News

Abstract

This paper explores the application of computational methods to the analysis of the large and growing corpus of news articles and related data on climate change. Topics are analyzed using Latent Dirichlet Allocation and methods customized to specific news sources that take advantage of keywords and other metadata that may be present. Results of this method on news articles drawn over several months are presented.