@inproceedings{santos-vieira-2017-pln,
    title = "{PLN}-{PUCRS} at {E}mo{I}nt-2017: Psycholinguistic features for emotion intensity prediction in tweets",
    author = "Santos, Henrique  and
      Vieira, Renata",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      van der Goot, Erik",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/W17-5225/",
    doi = "10.18653/v1/W17-5225",
    pages = "189--192",
    abstract = "Linguistic Inquiry and Word Count (LIWC) is a rich dictionary that map words into several psychological categories such as Affective, Social, Cognitive, Perceptual and Biological processes. In this work, we have used LIWC psycholinguistic categories to train regression models and predict emotion intensity in tweets for the EmoInt-2017 task. Results show that LIWC features may boost emotion intensity prediction on the basis of a low dimension set."
}