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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The sentiment classifier based on RoBERTa was fine-tuned for sentiment classification and provides strong sentiment analysis performance with a neutral sentiment class.
“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]
Section 4.1 (page unavailable) says the evaluated language models use the RoBERTa-base architecture, includes sentiment analysis among the classification tasks, and reports competitive sentiment results in Table 2. It describes training models for each task, but does not specify a neutral sentiment class or establish that the citing authors used one of these evaluated classifiers to label their sentences. (LLM, conf=0.82)
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- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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