@inproceedings{vettigli-sorgente-2021-empna,
    title = "{E}mp{N}a at {WASSA} 2021: A Lightweight Model for the Prediction of Empathy, Distress and Emotions from Reactions to News Stories",
    author = "Vettigli, Giuseppe  and
      Sorgente, Antonio",
    editor = "De Clercq, Orphee  and
      Balahur, Alexandra  and
      Sedoc, Joao  and
      Barriere, Valentin  and
      Tafreshi, Shabnam  and
      Buechel, Sven  and
      Hoste, Veronique",
    booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.wassa-1.28/",
    pages = "264--268",
    abstract = "This paper describes our submission for the WASSA 2021 shared task regarding the prediction of empathy, distress and emotions from news stories. The solution is based on combining the frequency of words, lexicon-based information, demographics of the annotators and personality of the annotators into a linear model. The prediction of empathy and distress is performed using Linear Regression while the prediction of emotions is performed using Logistic Regression. Both tasks are performed using the same features. Our models rank 4th for the prediction of emotions and 2nd for the prediction of empathy and distress. These results are particularly interesting when considered that the computational requirements of the solution are minimal."
}