Publications 

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Learn More about Text Mining and Associated Algorithms

• Beri, A. (2021, January 27). Stemming vs Lemmatization. Medium. https://towardsdatascience.com/stemming-vs-lemmatization-2daddabcb221

• 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. https://doi.org/10.1145/253262.253325

• George, Crissandra J., "Ambiguous Appalachianness: A Linguistic and Perceptual Investigation Into Arc-labeled Pennsylvania Counties" (2022). Theses and Dissertations-- Linguistics. 48. https://doi.org/10.13023/etd.2022.217

• Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794. https://doi.org/10.48550/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.  https://jusst.org/an-interpretation-of-lemmatization-and-stemming-in-natural-language-processing/

• 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. https://doi.org/10.1007/s11192-019-03137-5

• Lamba, M., & Madhusudhan, M. (2021, July 31). Text Pre-Processing. Text Mining for Information Professionals, 79–103. https://doi.org/10.1007/978-3-030-85085-2_3

• Lamba, M., & Madhusudhan, M. (2021, July 31). Topic Modeling. Text Mining for Information Professionals, 105–137. https://doi.org/10.1007/978-3-030-85085-2_4

• Santosa, F. A. (2023). Adding Perspective to the Bibliometric Mapping Using Bidirected Graph. Open Information Science, 7(1), 20220152. https://doi.org/10.1515/opis-2022-0152

• Santosa, F. A. (2023). Prior steps into knowledge mapping: Text mining application and comparison. Issues in Science and Technology Librarianship, 102. https://doi.org/10.29173/istl2736

• Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces. https://doi.org/10.3115/v1/w14-3110

• 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. https://doi.org/10.1145/2488388.2488514