In this paper we first propose a state of the art on the methods for the visualization and the interpretation of textual data, in particular of scientific data. We then shortly present our contributions to this field in the form of original methods for the automatic classification of documents and easy interpretation of their content through characteristic keywords and classes created by our algorithms. In a second step, we focus our analysis on the data evolving over time. We detail our di-achronic approach, especially suitable for the detection and visualization of topic changes. This allows us to conclude with Diachronic'Explorer, our upcoming tool for visual exploration of evolutionary data.