Sentiment is all you need to win US Presidential elections
Not on the map. Why?
Abstract (arXiv)
Election speeches play an integral role in communicating the vision and mission of the candidates. From lofty promises to mud-slinging, the electoral candidate accounts for all. However, there remains an open question about what exactly wins over the voters. In this work, we used state-of-the-art natural language processing methods to study the speeches and sentiments of the Republican candidate, Donald Trump, and Democratic candidate, Joe Biden, fighting for the 2020 US Presidential election. Comparing the racial dichotomy of the United States, we analyze what led to the victory and defeat of the different candidates. We believe this work will inform the election campaigning strategy and provide a basis for communicating to diverse crowds.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 34 references that are in the map. First posted on arXiv 27 Sep 2022.
Builds on
5 of 34 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 479
- 2020OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddingscited by 40
- 2018Learning to Map Context-Dependent Sentences to Executable Formal Queriescited by 23
- 2021Uncovering the Limits of Text-based Emotion Detectioncited by 8
Built on by
Citing papers per year: 2024: 1.
1 paper.