@inproceedings{andrew-2024-judithjeyafreeda,
    title = "{J}udith{J}eyafreeda{\_}{S}tress{I}dent{\_}{LT}-{EDI}@{EACL}2024: {GPT} for stress identification",
    author = "Andrew, Judith Jeyafreeda",
    editor = {Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Durairaj, Thenmozhi  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel},
    booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.ltedi-1.18/",
    pages = "173--176",
    abstract = "Stress detection from social media texts has proved to play an important role in mental health assessments. People tend to express their stress on social media more easily. Analysing and classifying these texts allows for improvements in development of recommender systems and automated mental health assessments. In this paper, a GPT model is used for classification of social media texts into two classes - stressed and not-stressed. The texts used for classification are in two Dravidian languages - Tamil and Telugu. The results, although not very good shows a promising direction of research to use GPT models for classification."
}