Replication Data for: Creating and Comparing Dictionary, Word Embedding, and Transformer-based Models to Measure Discrete Emotions in German Political Text

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 non-political 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 pre-trained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.

This replication directory contains code and data necessary to reproduce all models, figures, and tables included in "Creating and Comparing Dictionary, Word Embedding, and Transformer-based Models to Measure Discrete Emotions in German Political Text" as well as its supplemental online appendix.