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SemEval-2018 Task 1: Affect in Tweets
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We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet.For each task, we created labeled data from English, Arabic, and Spanish tweets.The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification.Seventy-five teams (about 200 team members) participated in the shared task.We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful.We also analyze systems for consistent bias towards a particular race or gender.The data is made freely available to further improve our understanding of how people convey emotions through language.
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“However, most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions [7], [9], [10].”
From Emotion Classification With Visibility Graphs (2025) · cites this paper as [9]
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Metadata
- Authors
- Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S.
- Affiliations
- Carnegie Mellon University Qatar (QA)
- National Research Council Canada (CA)
- Venue
- Proceedings of The 12th International Workshop on Semantic Evaluation
- Published
- 2018
- Pages
- 1-17
- OpenAlex
- W2805744755
- Cited by
- 746
- References
- 42
- Citations by year
- 2026: 49 · 2025: 64 · 2024: 85 · 2023: 104 · 2022: 98 · 2021: 107 · 2020: 87 · 2019: 81 · 2018: 70
- FWCI
- 35.21
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Workshop on Semantic Evaluation (SemEval) (publishedVersion, cc-by)
- NPARC (submittedVersion, cc-by)
- INFM-OAR (INFN Catania) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- affective computing, emotion recognition, tweets, social media text, sentiment analysis, emotion classification, shared task, Arabic tweets, Spanish tweets, English-language tweets, algorithmic bias, gender bias, racial bias
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.
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