From Noise to Nuance: Enriching Subjective Data Annotation through Qualitative Analysis

Ruyuan Wan, Haonan Wang, Ting-Hao Kenneth Huang, Jie Gao


Abstract
Subjective data annotation (SDA) plays an important role in many NLP tasks, including sentiment analysis, toxicity detection, and bias identification. Conventional SDA often treats annotator disagreement as noise, overlooking its potential to reveal deeper insights. In contrast, qualitative data analysis (QDA) explicitly engages with diverse positionalities and treats disagreement as a meaningful source of knowledge. In this position paper, we argue that human annotators are a key source of valuable interpretive insights into subjective data beyond surface-level descriptions. Through a comparative analysis of SDA and QDA methodologies, we examine similarities and differences in task nature (e.g., human’s role, analysis content, cost, and completion conditions) and practice (annotation schema, annotation workflow, annotator selection, and evaluation). Based on this comparison, we propose five practical recommendations for enabling SDA to capture richer insights. We demonstrate these recommendations in a reinforcement learning from human feedback (RLHF) case study and envision that our interdisciplinary perspective will offer new directions for the field.
Anthology ID:
2025.hcinlp-1.20
Volume:
Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Su Lin Blodgett, Amanda Cercas Curry, Sunipa Dev, Siyan Li, Michael Madaio, Jack Wang, Sherry Tongshuang Wu, Ziang Xiao, Diyi Yang
Venues:
HCINLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
240–254
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.hcinlp-1.20/
DOI:
Bibkey:
Cite (ACL):
Ruyuan Wan, Haonan Wang, Ting-Hao Kenneth Huang, and Jie Gao. 2025. From Noise to Nuance: Enriching Subjective Data Annotation through Qualitative Analysis. In Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP), pages 240–254, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
From Noise to Nuance: Enriching Subjective Data Annotation through Qualitative Analysis (Wan et al., HCINLP 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.hcinlp-1.20.pdf