Abstract
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.
Funder
European University Institute
Publisher
Cambridge University Press (CUP)
Subject
Political Science and International Relations,Sociology and Political Science
Reference64 articles.
1. The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information
2. Devlin, J. , Chang, M.-W. , Lee, K. , and Toutanova, K. . 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” Preprint, arXiv:1810.04805 [Cs].
3. He, P. , Liu, X. , Gao, J. , and Chen, W. . 2020. “Deberta: Decoding-Enhanced Bert with Disentangled Attention.” Preprint, arXiv:2006.03654.
4. Irrelevant events affect voters' evaluations of government performance
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献