Name: SemEval-2018 Task 1
Description: it creates a new Affect in Tweets dataset of more than 22,000 tweets such
  that subsets are annotated for a number of emotion dimensions.
Provider: Multiple institutions
Paper Title: SemEval-2018 Task 1: Affect in Tweets
Tasks: emotion intensity regression, emotion intensity classification, valence regression, 
  valence classification, emotion classification
Venue Name: International Workshop on Semantic Evaluation
Authors: Saif M. 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.