In this article I analyse parliamentary debates of the Finnish Parliament (Eduskunta) on European integration from 1990 to 2020. Finland joined the European Union (EU) in 1995, but Finland’s integration history dates back to the late 1950s and early 1960s. In the turbulent years following the end of the Cold War and the collapse of the Soviet Union in the early 1990s, European integration rose higher on the Finnish political agenda.

The data used in this article consist of a machine-readable database of plenary protocols of the Finnish Parliament. The main database covers the whole lifespan of the modern Finnish Parliament since 1906. The dataset used for the analysis contains all plenary speeches
with references to “Europe”, “European” and “Europeanism” (N=25,674), together with adequate metadata. The core analysis focuses on six time windows, each with a span of three years. These focus widows are linked to nationally important key European events.

Methodologically, the article is rooted in Exploratory Data Analysis (EDA) and applies different text mining tools to explore, analyse and visualise how members of the Finnish Eduskunta politicise and debate European issues. The analysis is carried out in three steps. First, I use traditional term-based text mining methods to explore the vocabulary used in the debate, both across time and by parliamentary faction. In the second step, I use tf-idf analysis to explore the vocabulary differentiating parliamentary factions. The analysis is rounded out in the third step by the application of Text Network Analysis (TNA). I apply TNA to explore and visualise topics in the collection of plenary speeches and, thus, to evidence the power and usefulness of this novel method as a complement to other topic modelling methods.

Overall, the results presented in this article find strong support when critically reflected against findings from previous studies. The results also significantly improve our knowledge and understanding of national parliamentary debates on European integration. Further, the article is encouraging when it comes to the application of computational methods and tools
on large corpora of unstructured political texts.