Abuzayed, A. and Al-Khalifa, H. (2021). “BERT for Arabic topic modeling: An experimental study on BERTopic technique.” Procedia Computer Science, Elsevier, Vol. 189, pp. 191-194, https://doi.org/10.1016/j.procs.2021.05.096.

Blei, D. M. (2012). “Probabilistic topic models.” Communica- tions of the ACM, ACM, Vol. 55, No. 4, pp. 77-84, https://doi.org/10.1145/2133806.2133826.

Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). “Latent dirichlet allocation.” Journal of Machine Learning Research, Vol. 3, pp. 993-1022.

Glez-Peña, D., Lourenço, A., López-Fernández, H., Reboiro- Jato, M. and Fdez-Riverola, F. (2014). “Web scraping technologies in an API world.” Briefings in Bioinformatics, Oxford Unversity, Vol. 15, No. 5, pp. 788-797, https://doi.org/10.1093/bib/bbt026.

Goldszmidt, R. G. B., Brito, L. A. L. and de Vasconcelos, F. C. (2011). “Country effect on firm performance: A multilevel approach.” Journal of Business Research, Elsevier, Vol. 64, No. 3, pp. 273-279, https://doi.org/10.1016/j.jbusres.2009.11.012.

Grootendorst, M. (2022). “BERTopic: Neural topic modeling with a class-based TF-IDF procedure.” arXiv Preprint, arXiv: 2203.05794 [cs.CL], https://doi.org/10.48550/arXiv.2203.05794.

Jallan, Y., Brogan, E., Ashuri, B. and Clevenger, C. M. (2019). “Application of natural language processing and text mining to identify patterns in construction-defect litigation cases.” Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, ASCE, Vol. 11, No. 4, 04519024, https://doi.org/10.1061/(ASCE)LA.1943-4170.0000308.

Javernick-Will, A. N. and Scott, W. R. (2010). “Who needs to know what? Institutional knowledge and global projects.” Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 5, pp. 546-557, https://doi.org/10.1061/(ASCE)CO.1943-7862.0000035.

Jiang, H. C., Qiang, M. S. and Lin, P. (2016). “Finding academic concerns of the Three Gorges Project based on a topic modeling approach.” Ecological Indicators, Elsevier, Vol. 60, pp. 693-701, https://doi.org/10.1016/j.ecolind.2015.08.007.

Jung, N. and Lee, G. (2019). “Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning.” Advanced Engineering Informatics, Elsevier, Vol. 41, 100917, https://doi.org/10.1016/j.aei.2019.04.007.

Moon, S., Chung, S. and Chi, S. (2018). “Topic modeling of news article about international construction market using latent dirichlet allocation.” KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 38, No. 4, pp. 595-599, https://doi.org/10.12652/Ksce.2018.38.4.0595 (in Korean).

Newman, D., Lau, J. H., Grieser, K. and Baldwin, T. (2010). “Automatic evaluation of topic coherence.” Proceedings of Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for ACL, ACL, Los Angeles, USA, pp. 100-108.

Stevens, K., Kegelmeyer, P., Andrzejewski, D. and Buttler, D. (2012). “Exploring topic coherence over many models and many topics.” Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, ACL, Jeju Island, Korea, pp. 952-961.

Wallach, H. M., Murray, I., Salakhutdinov, R. and Mimno, D. (2009). “Evaluation methods for topic models.” Proceedings of the 26th Annual International Conference on Machine Learning, Association for Computing Machinery, ACM, New York, USA, pp. 1105-1112, https://doi.org/10.1145/1553374.1553515.

Wei, X. and Croft, W. B. (2006). “LDA-based document models for ad-hoc retrieval.” Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, USA, pp. 178-185, https://doi.org/10.1145/1148170.1148204.