@inproceedings{mohammad-etal-2018-semeval,
title = "{S}em{E}val-2018 Task 1: Affect in Tweets",
author = "Mohammad, Saif and
Bravo-Marquez, Felipe and
Salameh, Mohammad and
Kiritchenko, Svetlana",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1001",
doi = "10.18653/v1/S18-1001",
pages = "1--17",
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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%0 Conference Proceedings
%T SemEval-2018 Task 1: Affect in Tweets
%A Mohammad, Saif
%A Bravo-Marquez, Felipe
%A Salameh, Mohammad
%A Kiritchenko, Svetlana
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F mohammad-etal-2018-semeval
%X 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.
%R 10.18653/v1/S18-1001
%U https://aclanthology.org/S18-1001
%U https://doi.org/10.18653/v1/S18-1001
%P 1-17
Markdown (Informal)
[SemEval-2018 Task 1: Affect in Tweets](https://aclanthology.org/S18-1001) (Mohammad et al., SemEval 2018)
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.