Word Clustering for Historical Newspapers Analysis

This paper is a part of a collaboration between computer scientists and historians aimed at development of novel methods for historical newspapers analysis. We present a case study of ideological terms ending with -ism suffix in nineteenth-century Finnish newspapers. We propose a two-step procedure to trace differences in word usages over time: training of diachronic embeddings on several time slices and when clustering embeddings of selected words together with their neighbours to obtain historical context. The obtained clusters turn out to be useful for historical studies. The paper also discusses specific difficulties related to the development of historian-oriented tools.