Creating and comparing dictionary, word embedding, and transformer-based models to measure discrete emotions in German political text
Political analysis, 2023, Vol. 31, No. 4, pp. 626-641
WIDMANN, Tobias, WICH, Maximilian, Creating and comparing dictionary, word embedding, and transformer-based models to measure discrete emotions in German political text, Political analysis, 2023, Vol. 31, No. 4, pp. 626-641 - https://hdl.handle.net/1814/75397
Retrieved from Cadmus, EUI Research Repository
Previous 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.
Published online: 29 June 2022
Cadmus permanent link: https://hdl.handle.net/1814/75397
Full-text via DOI: 10.1017/pan.2022.15
ISSN: 1047-1987; 1476-4989
Publisher: Cambridge University Press
Sponsorship and Funder information:
This article was published Open Access with the support from the EUI Library through the CRUI - CUP Transformative Agreement (2020-2022)
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