@inproceedings{mohammad-bravo-marquez-2017-emotion,
    title = "Emotion Intensities in Tweets",
    author = "Mohammad, Saif  and
      Bravo-Marquez, Felipe",
    editor = "Ide, Nancy  and
      Herbelot, Aur{\'e}lie  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*{SEM} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-1007/",
    doi = "10.18653/v1/S17-1007",
    pages = "65--77",
    abstract = "This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best{--}worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity; and, the extent to which two emotions are similar in terms of how they manifest in language."
}