SemEval-2018 Task 1: Affect in Tweets

Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko


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.
Anthology ID:
S18-1001
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–17
Language:
URL:
https://aclanthology.org/S18-1001
DOI:
10.18653/v1/S18-1001
Bibkey:
Cite (ACL):
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018. SemEval-2018 Task 1: Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1–17, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
SemEval-2018 Task 1: Affect in Tweets (Mohammad et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1001.pdf
Note:
 S18-1001.Notes.pdf