Recommended Reading

Beri, A. (2021, January 27). Stemming vs Lemmatization. Medium.

Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. ACM SIGMOD Record, 26(2), 255–264.

George, Crissandra J., "Ambiguous Appalachianness: A Linguistic and Perceptual Investigation Into Arc-labeled Pennsylvania Counties" (2022). Theses and Dissertations-- Linguistics. 48.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.

Khyani, D., Siddhartha B S, Niveditha N M, & Divya B M. (2020). An Interpretation of Lemmatization and Stemming in Natural Language Processing. Journal of University of Shanghai for Science and Technology , 22(10), 350–357.

Lamba, M., & Madhusudhan, M. (2019, June 7). Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study. Scientometrics, 120(2), 477–505.

Lamba, M., & Madhusudhan, M. (2021, July 31). Text Pre-Processing. Text Mining for Information Professionals, 79–103.

Lamba, M., & Madhusudhan, M. (2021, July 31). Topic Modeling. Text Mining for Information Professionals, 105–137.

Santosa, F. A. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152.

Santosa, F. A. (2023). Prior steps into knowledge mapping: Text mining application and comparison. Issues in Science and Technology Librarianship, 102.

Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces.

Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013, May 13). A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web.