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The only resource available in the public domain which highlights parliamentary ac tivity is parliamentary questions. Up until the last ten years, manual content analysis was carried out to classify these. More recently, machine learning techniques have been used to automatically classify and analyse these data sets. This study analyses the verbal parliamentary speeches in the Irish Parliament (known as the D´ail) over a ten year period using unsupervised machine learning. It does so by applying a less utilised topic modeling technique, known as Non-negative Matrix Factorisation (NMF), to de tect the latent themes in these speeches. A two-layer dynamic approach using NMF is applied to extract the themes raised in these speeches at a point in time and over the entire period. The ﬁndings suggest that the themes raised vary from very niche subject matter areas to more general areas and have evolved over time. The trend in the topics raised over the entire period give an indication of what the political agenda was during these Da´il terms. Furthermore, reviewing the topics at a party and indi vidual TD level demonstrate what their political priorities are. Conversely, reviewing the topics that parties and TDs are not discussing gives an insight into the themes that they have no interest in.
Leheny, Fiona (2018). An exploration of parliamentary speeches in the Irish parliament using topic modeling. Masters dissertation, DIT, 2018.