Cited by 746Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionMental Health via Writing

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SemEval-2018 Task 1: Affect in Tweets

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Mohammad, Saif, Bravo-Marquez, Felipe, Salameh, Mohammad, Kiritchenko, Svetlana·2018·Proceedings of The 12th International Workshop on Semantic Evaluation

DOI 10.18653/v1/s18-1001

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Abstract

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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Figure 1 Valence score (V-reg), class (V-oc) distribution.CC-BY · From the PDF

Figure 5 Emotion intensity score (EI-reg) and ordinal class (EI-oc) distributions for the four basic emotions in the SemEval2018 AIT development and test sets combined. The distribution is similar for the training set (annotated in earlier work).CC-BY · From the PDF

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Authors: Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S.
Affiliations: Carnegie Mellon University Qatar (QA)
National Research Council Canada (CA)
ORCID: Mohammad, Saif
Salameh, Mohammad
Kiritchenko, Svetlana
Venue: Proceedings of The 12th International Workshop on Semantic Evaluation
Publisher: Association for Computational Linguistics
Published: 2018
Pages: 1-17
DOI: 10.18653/v1/s18-1001
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

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