@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T To Labor is Not to Suffer: Exploration of Polarity Association Bias in LLMs for Sentiment Analysis
%A Chen, Jiyu
%A Karimi, Sarvnaz
%A Molla, Diego
%A Paris, Cecile
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S 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
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-299-2
%F chen-etal-2025-labor
%X 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.
%U https://aclanthology.org/2025.ijcnlp-short.6/
%P 70-78
Markdown (Informal)
[To Labor is Not to Suffer: Exploration of Polarity Association Bias in LLMs for Sentiment Analysis](https://aclanthology.org/2025.ijcnlp-short.6/) (Chen et al., IJCNLP-AACL 2025)
ACL