Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding

Cesare Spinoso-Di Piano, Samira Rahimi, Jackie Cheung


Abstract
Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.
Anthology ID:
2023.findings-emnlp.993
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14887–14909
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.993
DOI:
10.18653/v1/2023.findings-emnlp.993
Bibkey:
Cite (ACL):
Cesare Spinoso-Di Piano, Samira Rahimi, and Jackie Cheung. 2023. Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14887–14909, Singapore. Association for Computational Linguistics.
Cite (Informal):
Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding (Spinoso-Di Piano et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-emnlp.993.pdf