In this paper, we develop a visualization tool that applies unsupervised named entity recognition and streamgraphs in order to visualize massive amounts of unstructured textual stream data, namely, French newspapers (e.g. Le Figaro, La presse, L'humanité) from the first world war period. Such a visualization allows us to identify main characters, events and locations involved in or relevant to the first world war, according to the French press. Furthermore, our visualization technique can help visually identify correlations between major people (e.g. presidents, generals, public figures...), locations (e.g. countries, cities, towns...) and organizations and events (e.g. corporations, battles...) on multiple aligned streamgraphs. We discuss the streamgraph method and compare it to alternative methods while drawing some examples from the visualization of the first world war. Our method helps users identify named entities and visualise them highlighting significant ones at specific time periods. Furthermore, it can be applied to unstructured data streams of any domain or time period.