Mohammad, Saif M.Bravo-Marquez, Felipe2017-11-2420172017-11-242017Mohammad, S. M., & Bravo-Marquez, F. (2017). Emotion intensities in Tweets. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017) (pp. 65–77). Vancouver, Canada: Association for Computational Linguistics. https://doi.org/10.18653/v1/S17-1007978-1-945626-53-1https://hdl.handle.net/10289/11513This 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.application/pdfenThis is an author’s accepted version of an article published in the Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017). © 2017 Association for Computational Linguisticcomputer scienceConference Contribution10.18653/v1/S17-1007