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We build a sentence-level political discourse classifier using existing humanexpert annotated corpora of political manifestos from the Manifestos Project(Volkens et al., 2020a) and applying them to a corpus ofCOVID-19Press Briefings(Chatsiou, 2020). We use manually annotated political manifestos as trainingdata to train a local topic ConvolutionalNeural Network (CNN) classifier; thenapply it to the COVID-19PressBriefings Corpus to automatically classifysentences in the test corpus.We report on a series of experiments with CNNtrained on top of pre-trained embeddings for sentence-level classificationtasks. We show thatCNN combined with transformers like BERT outperforms CNNcombined with other embeddings (Word2Vec, Glove, ELMo) and that it is possibleto use a pre-trained classifier to conduct automatic classification ondifferent political texts without additional training.

Text Classification of Manifestos and COVID-19 Press Briefings using BERT and Convolutional Neural Networks