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Automated Multi-Label Classification of the Dutch Speeches from the Throne

For social science researchers, qualitative content analysis and classification is a time consuming and costly endeavor. The Dutch Speeches from the Throne (1945–2010), for example, constitute a data set of 9143 sentences and 11779 unique words. Manually labeling these sentences is a cumbersome task. As part of the Political Attention Radar this thesis presents an automated Dutch multi-label classification system. The presented solution uses a traditional bag-of-words document representation, an extensive set of human coded examples, and an exhaustive topic coding system to automatically classify each sentence into none, one or several categories. For social science researchers this thesis contributes a new view on the data through an automated classification system. For computer science it contributes a multi-labeling approach to existing classification solutions.