Topic Identification and Analysis in Large News Corpora

The media today bombards us with massive amounts of news about events ranging from the mundane to the memorable. This growing cacophony places an ever greater premium on being able to identify significant stories and to capture their salient features. In this paper, we consider the problem of mining on-line news over a certain period to identify what the major stories were in that time. Major stories are defined as those that were widely reported, persisted for significant duration or had a lasting influence on subsequent stories. Recently, some statistical methods have been proposed to extract important information from large corpora, but most of them do not consider the full richness of language or variations in its use across multiple reporting sources. We propose a method to extract major stories from large news corpora using a combination Latent Dirichlet Allocation and with n-gram analysis.