Theory-Grounded Computational Text Analysis

Arya D. McCarthy, Giovanna Maria Dora Dore


Abstract
In this position paper, we argue that computational text analysis lacks and requires organizing principles. A broad space separates its two constituent disciplines—natural language processing and social science—which has to date been sidestepped rather than filled by applying increasingly complex computational models to problems in social science research. We contrast descriptive and integrative findings, and our review of approximately 60 papers on computational text analysis reveals that those from *ACL venues are typically descriptive. The lack of theory began at the area’s inception and has over the decades, grown more important and challenging. A return to theoretically grounded research questions will propel the area from both theoretical and methodological points of view.
Anthology ID:
2023.acl-short.136
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1586–1594
Language:
URL:
https://aclanthology.org/2023.acl-short.136
DOI:
10.18653/v1/2023.acl-short.136
Bibkey:
Cite (ACL):
Arya D. McCarthy and Giovanna Maria Dora Dore. 2023. Theory-Grounded Computational Text Analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1586–1594, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Theory-Grounded Computational Text Analysis (McCarthy & Dore, ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-short.136.pdf