Abstract
Inductive content analysis is a research task in which a researcher manually reads text and identifies categories or themes that emerge from a document corpus. Inductive content analysis is usually performed as part of a formal qualitative research methodology such as Grounded Theory. Topic modeling algorithms discover the latent topics in a document corpus. There has been a general assumption, that topic modeling is a suitable algorithmic aid for inductive content analysis. In this short paper, the findings from a between-subjects experiment to evaluate the differences between topics identified by manual coders and topic modeling algorithms is discussed. The findings show that the topic modeling algorithm was only comparable to the human coders for broad topics and that topic modeling algorithms would require additional domain knowledge in order to identify more fine-grained topics. The paper also reports issues that impede the use of topic modeling within the quantitative ethnography process such as topic interpretation and topic size quantification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bakharia, A., Bruza, P., Watters, J., Narayan, B., Sitbon, L.: Interactive topic modeling for aiding qualitative content analysis. In: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval, pp. 213–222. ACM (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J.L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)
Hsieh, H.F., Shannon, S.E.: Three approaches to qualitative content analysis. Qual. Health Res. 15(9), 1277–1288 (2005)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556–562 (2001)
Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756–2779 (2007)
Sievert, C., Shirley, K.: LDAVis: a method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70 (2014)
Acknowledgement
The experiments described within this paper were conducted as part of my doctorate degree at Queensland University of Technology. I would like to thank and acknowledge my supervisors Peter Bruza, Jim Watters, Bhuva Narayan and Laurianne Sitbon.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bakharia, A. (2019). On the Equivalence of Inductive Content Analysis and Topic Modeling. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-33232-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33231-0
Online ISBN: 978-3-030-33232-7
eBook Packages: Computer ScienceComputer Science (R0)