@InProceedings{mohammad-EtAl:2018:S18-1,
  author    = {Mohammad, Saif  and  Bravo-Marquez, Felipe  and  Salameh, Mohammad  and  Kiritchenko, Svetlana},
  title     = {SemEval-2018 Task 1: Affect in Tweets},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S18-1001}
}