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TimeLMs: Diachronic Language Models from Twitter
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Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-collados. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2022.
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“We then classified the sentences into positive, neutral or negative using a sentiment classifier based on RoBERTa49, which was fine-tuned for sentiment classification50.”
From Generative language models exhibit social identity biases (2024) · cites this paper as [50]
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Self-citation
Research works printed but not linked: 1
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
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Generative language models exhibit social identity biases
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“We then classified the sentences into positive, neutral, or negative with a sentiment classification RoBERTa model [45] and assessed a more fine-grained sentence sentiment score using VADER [46].”
Metadata
- Authors
- Loureiro, D., Barbieri, F., Neves, L., Espinosa Anke, L., & Camacho-collados, J.
- Affiliations
- Universidade do Porto (PT)
- Venue
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Published
- 2022
- Pages
- 251-260
- OpenAlex
- W4285190530
- Cited by
- 263
- References
- 36
- Citations by year
- 2026: 55 · 2025: 66 · 2024: 68 · 2023: 51 · 2022: 23
- FWCI
- 28.76
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) (publishedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- Twitter, social media text, language change
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