References
- Patterson, Clare M., Jason RC Nurse, and Virginia NL Franqueira. "Learning from cyber security incidents: A systematic review and future research agenda." Computers & Security (2023): 103309.
- Grootendorst, Maarten. "BERTopic: Neural topic modeling with a class-based TF-IDF procedure." arXiv preprint arXiv:2203.05794 (2022).
- Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.
- Egger, R., & Yu, J. (2021). Identifying hidden semantic structures in Instagram data: a topic modelling comparison. Tourism Review, 77(4), 1234-1246.
- Yu, Dejian, and Bo Xiang. "Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling." Expert Systems with Applications (2023): 120114.
- Abdelrazek, A., Eid, Y., Gawish, E., Medhat, W., & Hassan, A. (2023). Topic modeling algorithms and applications: A survey. Information Systems, 112, 102131.
- Gan, Lin, et al. "Experimental Comparison of Three Topic Modeling Methods with LDA, Top2Vec and BERTopic." International Symposium on Artificial Intelligence and Robotics. Singapore: Springer Nature Singapore, 2023.
- Zengul, Ferhat, et al. "A practical and empirical comparison of three topic modeling methods using a COVID-19 corpus: LSA, LDA, and Top2Vec." (2023).
- Borcin, Martin, and Joemon M. Jose. "Optimizing BERTopic: Analysis and Reproducibility Study of Parameter Influences on Topic Modeling." European Conference on Information Retrieval. Cham: Springer Nature Switzerland, 2024.
- An, Yusung, Hayoung Oh, and Joosik Lee. "Marketing insights from reviews using topic modeling with BERTopic and deep clustering network." Applied Sciences 13.16 (2023): 9443.