Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      WASSA-2017 shared task on emotion intensity

      Mohmmad, Saif M.; Bravo-Marquez, Felipe
      Thumbnail
      Files
      W17-5205.pdf
      374.9Kb
      Link
       aclweb.org
      Find in your library  
      Citation
      Export citation
      Mohmmad, S. M., & Bravo-Marquez, F. (2017). WASSA-2017 shared task on emotion intensity. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 34–49). Copenhagen, Denmark: Association for Computational Linguistics.
      Permanent Research Commons link: https://hdl.handle.net/10289/12366
      Abstract
      We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.
      Date
      2017
      Type
      Conference Contribution
      Publisher
      Association for Computational Linguistics
      Rights
      © 2017 Association for Computational Linguistics. Used with permission.
      Collections
      • Computing and Mathematical Sciences Papers [1454]
      Show full item record  

      Usage

      Downloads, last 12 months
      90
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement