Automatic thematic classification of election manifestos
We digitized political texts from the 1980s and 1990s. We used these data to learn a classifier that can label more recent political texts. Change of themes over the years affects recall of the learned classifier. But precision is comparable to the precision obtained by a human expert labeller. For political themes, a high level of detail seems to be preferred by domain experts.
text classification political data expert evaluation