Journal of Information and Knowledge (Formerly SRELS Journal of Information Management)

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

AI-Based Literature Reviews : A Topic Modeling Approach


Affiliations
1 Department of Library and Information Science, Mizoram University, Aizwal - 796004, Mizoram, India
2 Central Library, Central University of South Bihar, Gaya – 824236, Bihar, India
     

   Subscribe/Renew Journal


The purpose of this paper is to highlight the importance of topic modelling in conducting literature reviews using the open-source LDAShiny package in the R environment, with green libraries literature as a case study. To conduct the analysis, a title and abstract dataset were prepared using the Scopus database and imported into the LDAShiny package for further analysis. It was found that the green libraries' literature ranged from 1989-2023, with a sharp increase in research topics since 2003. The study also identified key themes and documents associated with green libraries research, revealing that energy efficiency, waste reduction and recycling, and the use of sustainable materials have been extensively discussed in the literature. However, further research is needed on the implementation of these practices in libraries, as well as the impact of the COVID-19 pandemic on green libraries. The findings will be beneficial to researchers interested in using topic modelling for literature reviews.

Keywords

Green Libraries, Latent Topics, LDA Shiny, Literature Review, Topic Modelling.
User
About The Authors

Manoj Kumar Verma
Department of Library and Information Science, Mizoram University, Aizwal - 796004, Mizoram
India

Mayank Yuvaraj
Central Library, Central University of South Bihar, Gaya – 824236, Bihar
India


Notifications

  • Adam, G.P., Wallace, B.C. and Trikalinos, T.A. (2022). Semi-automated tools for systematic searches. in: metaresearch. methods in molecular biology, edited by Evangelou, E., Veroniki, A.A. New York, NY: Humana; pp. 17-40. https://doi.org/10.1007/978-1-0716-1566-9_2 PMid:34550582

  • Ahmed, F. and Khan, A. (2022). Topic modeling as a tool to analyze child abuse from the corpus of english newspapers in Pakistan. Social Science Computer Review. OnlineFirst. https://doi.org/10.1177/08944393221132637

  • Antons, D., Breidbach, C. F., Joshi, A. M. and Salge, T. O. (2023). Computational literature reviews: Method, algorithms, and roadmap. Organizational Research Methods, 25, 107-138. https://doi.org/10.1177/1094428121991230

  • Asmussen, C.B. and Moller, C. (2019). Smart literature review: A practical topic modeling approach to exploratory literature review. Journal of Big Data, 6, 93. https://doi.org/10.1186/s40537-019-0255-7

  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. and Lim, W.M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070

  • Gan, J. and Qi, Y. (2021). Selection of the optimal number of topics for LDA topic model-taking patent policy as an example. Entropy, 23, 1-45. https://doi.org/10.3390/e23101301

  • Hoz-M, J. De La, Fernandez-Gomez, M. J. and Medes, S. (2021). LDAShiny: An R package for exploratory review of scientific literature based on Bayesian probabilistic model and machine learning tools. Mathematics, 9. https://doi.org/10.3390/math9141671

  • Kavvadias, S., Drosatos, G. and Kaldoudi, E. (2020). Supporting topic modeling and trend analysis in bio-medical literature. Journal of Biomedical Informatics, 110, 103574. https://doi.org/10.1016/j.jbi.2020.103574 PMid:32971274

  • Kunisch, S., Denyer, D., Bartunek, J. M., Menz, M. and Cardinal, L. B. (2023). Review research as scientific inquiry. Organizational Research Methods, 26, 3-45. https://doi.org/10.1177/10944281221127292

  • Lim, W.M., Yap, S.F. and Makkar, M. (2021). Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? Journal of Business Research, 122, 534-566, https://doi.org/10.1016/j.jbusres.2020.08.051 PMid:33012896 PMCid:PMC7523531

  • Marshall, I. J. and Wallace, B. C. (2019). Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Systematic Reviews, 8, 163. https://doi.org/10.1186/s13643-019-1074-9 PMid:31296265 PMCid:PMC6621996

  • Mostafa, M. (2022). A one-hundred-year structural topic modeling analysis of knowledge structure of international management research. Quality and Quantity. OnlineFirst. https://doi.org/10.1007/s11135-022-01548-w PMid:36249708 PMCid:PMC9549032

  • Mustak, M., Salminen, J., Ple, L. and Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis and research agenda. Journal of Business Research, 124, 389-404. https://doi.org/10.1016/j.jbusres.2020.10.044

  • Ozyurt, O. and Ayaz, A. (2022). Twenty-five years of education and information technologies: Insights from a topic modeling based bibliometric analysis. Education and Information Technologies, 27, 11025-11054. https://doi.org/10.1007/s10639-022-11071-y PMid:35502161 PMCid:PMC9046010

  • Paul, J., Lim, W.M. , O’Cass, A., Hao, A.W. and Bresciani, S. (2021). Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45, O1-O16, https://doi.org/10.1111/ijcs.12695

  • Saha, B. (2021). Application of topic modeling for literature review in management research. In: Interdisciplinary research in technology and management, edited by S. Chakrabarti, R. Nath, P. K. Banerji, S. Datta, S. Poddar and M. Gangopadhyaya. London: CRC Press; pp. 249-256.

  • Schmiedel, T., Muller, O. and Brocke, J.V. (2019). Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture. Organizational Research Methods, 22, 941-968. https://doi.org/10.1177/1094428118773858

  • Schoot, R. V., Bruin, J. Schram, R., Zahedi, P., Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoggerwerf, M., Ferdinands, G., Harkema, A., Willemsen, W., Ma, Y., Fang, Q., Hindriks, S., Tummers, L. and Oberski, D. L. (2021). An open source machine learning framework for efficient and transparent systematic reviews. Nature Machine Intelligence, 3, 125-133. https://doi.org/10.1038/s42256-020-00287-7

  • Snyder, H. (2019). Literature review as a research methodology: an overview and guidelines, Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039

  • Wagner, G., Lukyanenko, R. and Pare, G. (2022). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37, 209-226. https://doi.org/10.1177/02683962211048201

  • Wallace, B. C., Small, K., Brodley, C. E. and Trikalinos, T. A. (2010). Active learning for biomedical citation screening. In 16th ACM SIGKDD International Conference on Knowledge discovery and data mining, edited by B. Rao, B. Krishnapuram, A. Tomkins and Q. Yang, Washington DC, USA; pp. 173-182. https://doi.org/10.1145/1835804.1835829 PMid:20565949 PMCid:PMC2903585

  • Xie, Y., Ning, C. and Sun, L. (2022). The twenty-first century of structural engineering research: A topic modeling approach. Structures, 35, 577-590. https://doi.org/10.1016/j.istruc.2021.11.018


Abstract Views: 137

PDF Views: 1




  • AI-Based Literature Reviews : A Topic Modeling Approach

Abstract Views: 137  |  PDF Views: 1

Authors

Manoj Kumar Verma
Department of Library and Information Science, Mizoram University, Aizwal - 796004, Mizoram, India
Mayank Yuvaraj
Central Library, Central University of South Bihar, Gaya – 824236, Bihar, India

Abstract


The purpose of this paper is to highlight the importance of topic modelling in conducting literature reviews using the open-source LDAShiny package in the R environment, with green libraries literature as a case study. To conduct the analysis, a title and abstract dataset were prepared using the Scopus database and imported into the LDAShiny package for further analysis. It was found that the green libraries' literature ranged from 1989-2023, with a sharp increase in research topics since 2003. The study also identified key themes and documents associated with green libraries research, revealing that energy efficiency, waste reduction and recycling, and the use of sustainable materials have been extensively discussed in the literature. However, further research is needed on the implementation of these practices in libraries, as well as the impact of the COVID-19 pandemic on green libraries. The findings will be beneficial to researchers interested in using topic modelling for literature reviews.

Keywords


Green Libraries, Latent Topics, LDA Shiny, Literature Review, Topic Modelling.

References





DOI: https://doi.org/10.17821/srels%2F2023%2Fv60i2%2F170967