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dc.contributor.authorWIDMANN, Tobias
dc.contributor.authorWICH, Maximilian
dc.identifier.citationPolitical analysis, 2022, OnlineFirsten
dc.descriptionPublished online: 29 June 2022en
dc.description.abstractPrevious research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.en
dc.description.sponsorshipThis article was published Open Access with the support from the EUI Library through the CRUI - CUP Transformative Agreement (2020-2022)en
dc.publisherCambridge University Pressen
dc.relation.ispartofPolitical analysisen
dc.titleCreating and comparing dictionary, word embedding, and transformer-based models to measure discrete emotions in German political texten
dc.rights.licenseAttribution 4.0 International*

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International