Describing the language of the Dutch House of Representatives

Abstract

Political content reaches civilians via social media more and more nowadays. This content is often biased and filtered depending on the user’s connections on social media. However, almost nobody watches actual debates or reads reports from the actual Dutch House of Representatives (DHoR), where the most influential decisions are made. With our Bachelor’s thesis we aim to give users an insight into the language used by Representatives in the DHoR. This Bachelor thesis can be divided into two parts, the first part is the research phase, the second part the development of the Data Analysis Tool. During the research phase we searched for ways that would allow us to describe the use of language in the DHoR, and the resources necessary to perform corresponding calculations. We defined several non-topical text features to perform the description: complexity, sentiment analysis and the rate of femininity / masculinity. For each one of these features, we will calculate their respective values based on the particular language of a Representative. Besides these non-topical text features, we also introduce a topical text feature. As opposed to the first, topical text features actually focus on the content of a text, rather than the language used. Some politicians are known to act differently regarding certain circumstances. By introducing filters, we can explore the effects or influences these circumstances have on the use of language (text features) of Representatives. Finally, we examined the available data from the DHoR and how to access it.
The second part of this thesis is the development of our Data Analysis Tool (DAT). In order to calculate scores for the text features, we used reported data from debates and discussions in the DHoR from the past five years. We developed
a DAT that can be used to explore the use of language of Representatives by:
– analysing individual Representative text feature scores
– comparing the average text feature scores of all Representatives
– allowing to plot the text feature scores of Representatives over time and filter on time