Text classification of manifestos and COVID-19 press briefings using BERT and Convolutional Neural Networks

Chatsiou, Kakia (2020) Text classification of manifestos and COVID-19 press briefings using BERT and Convolutional Neural Networks. arxiv.org. (Submitted)

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Abstract

We build a sentence-level political discourse classifier using existing human expert annotated corpora of political manifestos from the Manifestos Project (Volkens et al., 2020a) and applying them to a corpus of COVID-19 Press Briefings (Chatsiou, 2020). We use manually annotated political manifestos as training data to train a local topic Convolutional Neural Network (CNN) classifier; then apply it to the COVID-19Press Briefings Corpus to automatically classify sentences in the test corpus. We report on a series of experiments with CNN trained on top of pre-trained embeddings for sentence-level classification tasks. We show that CNN combined with transformers like BERT outperforms CNN combined with other embeddings (Word2Vec, Glove, ELMo) and that it is possible to use a pre-trained classifier to conduct automatic classification on different political texts without additional training.

Item Type: Article
Uncontrolled Keywords: Covid-19, press briefings, Convolutional Neural Networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Health & Science > Department of Science & Technology
Depositing User: Dr Kakia Chatsiou
Date Deposited: 31 Oct 2022 15:35
Last Modified: 31 Oct 2022 15:35
URI: https://oars.uos.ac.uk/id/eprint/2134

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