In this paper we present primary content analysis results for the Greek Parliament data in the context of Natural Language Processing and Text Mining approaches. The raw minutes of the Greek Parliament plenary sessions of the last 26 years are processed and transformed into a structured and machine readable format, and then clustered based on the analysis of their content using topic modelling techniques. Inspired by and following the work of Greene and Gross (2017) for the European Parliament, we employ a two-layer methodology for applying topic modelling in a Non-negative Matrix Factorization framework to a timestamped corpus of political speeches in order to explore dynamic topics. The results are visualized in various ways (by topic, by time) providing at the same time information about the contribution of each Parliament Member, political party and region (constituency) to each topic, and by extent, the ability to explore how the political and policy agenda has been shaped and evolved in Greece over time.