Abstract
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.
Links
Paper: https://www.cambridge.org/core/journals/political-analysis/article/creating-and-comparing-dictionary-word-embedding-and-transformerbased-models-to-measure-discrete-emotions-in-german-political-text/2DA41C0F09DE1CA600B3DCC647302637
Twitter-Thread summarizing the Paper: https://twitter.com/TobiasWidmann/status/1542157031376822274
Github-Repository: https://github.com/tweedmann/3x8emotions
