The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Smart literature review: a practical topic modelling approach to exploratory literature review
Open accessAbstract
Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.
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Cites
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Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer's Disease
Escott-Price, V., Bellenguez, C., Wang, L.-S., Choi, S.-H., Harold, D., Jones, L., Holmans, P., Gerrish, A., Vedernikov, A., Richards, A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094661 · Cited by 1,398
The politics of attention: how government prioritizes problems
(2006) · Choice Reviews Online · doi:10.5860/choice.43-4321 · Cited by 1,160
DiMaggio, P., Nag, M., & Blei, D. (2013) · Poetics · doi:10.1016/j.poetic.2013.08.004 · Cited by 1,145
A correlated topic model of Science
Blei, D. M., & Lafferty, J. (2018) · doi:10.1184/r1/6587330.v1 · Cited by 1,088
A correlated topic model of Science
Blei, D. M., & Lafferty, J. D. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas114 · Cited by 953
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., et al. (2018) · Communication Methods and Measures · doi:10.1080/19312458.2018.1430754 · Cited by 832
How to analyze political attention with minimal assumptions and costs
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00427.x · Cited by 671
Cited by
Normalized Approach to Find Optimal Number of Topics in Latent Dirichlet Allocation (LDA)
Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., & Islam, M. J. (2021) · Advances in Intelligent Systems and Computing · doi:10.1007/978-981-33-4673-4_27 · Cited by 119
Analyzing the contributions of biofuels, biomass, and bioenergy to sustainable development goals
Raman, R., Sreenivasan, A., Kulkarni, N. V., Suresh, M., & Nedungadi, P. (2025) · iScience · doi:10.1016/j.isci.2025.112157 · Cited by 91
Characterization of False or Misleading Fluoride Content on Instagram: Infodemiology Study
Lotto, M., Sá Menezes, T., Zakir Hussain, I., Tsao, S.-F., Ahmad Butt, Z., P Morita, P., & Cruvinel, T. (2022) · Journal of Medical Internet Research · doi:10.2196/37519 · Cited by 76
Automation of Legal Precedents Retrieval: Findings from a Literature Review
Mentzingen, H., António, N., & Bacao, F. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/6660983 · Cited by 16
Metadata
- Authors
- Asmussen, C. B., & Møller, C.
- Venue
- Journal of Big Data
- Published
- 2019-10-19
- Volume
- 6
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W2981123275
- Cited by
- 455
- References
- 29
- FWCI
- 88.87
- Type
- journal article
- Language
- en
- Open access
- gold
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- Asmussen, C. B., & Møller, C. (2019). Smart literature review: a practical topic modelling approach to exploratory literature review. Journal of Big Data. https://doi.org/10.1186/s40537-019-0255-7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
