@inproceedings{chen-etal-2025-labor,
    title = "To Labor is Not to Suffer: Exploration of Polarity Association Bias in {LLM}s for Sentiment Analysis",
    author = "Chen, Jiyu  and
      Karimi, Sarvnaz  and
      Molla, Diego  and
      Paris, Cecile",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-short.6/",
    pages = "70--78",
    ISBN = "979-8-89176-299-2",
    abstract = "Large language models (LLMs) are widely used for modeling sentiment trends on social media text. We examine whether LLMs have a polarity association bias{---}positive or negative{---}when encountering specific types of lexical word mentions. Such polarity association bias could lead to the wrong classification of neutral statements and thus a distorted estimation of sentiment trends. We estimate the severity of the polarity association bias across five widely used LLMs, identifying lexical word mentions spanning a diverse range of linguistic and psychological categories that correlate with this bias. Our results show a moderate to strong degree of polarity association bias in these LLMs."
}