This study analyzed 928 full-text research articles retrieved from DESIDOC Journal of Library and Information Technology for the period of 1981–2018 using Latent Dirichlet Allocation. The study further tagged the articles with the modeled topics. 50 core topics were identified throughout the period of 38 years whereas only 26 topics were unique in nature. Bibliometrics, ICT, information retrieval, and user studies were highly researched areas in India for the epoch. Further, Spain and Taiwan showed common research trends and areas as India whereas India has quite distinct research interests from America and China. Therefore, researchers in Library and Information Science in India should pay more attention to the topics which are under-researched. Further, it was found that there were some unique sub-fields to Indian Library and Information Science research, such as open access; online exhibition; virtual libraries; multimedia libraries; open source software; library automation; and library management system. With the passage of time topics evolve over time, new topics emerge, and old ones become obsolete. Topic modeling not only helps the researcher to determine the trending themes or related fields with respect to their field of interest but also helps them to identify new concepts and fields over time.
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Abinaya, G., & Winster, S. G. (2014). Event identification in social media through latent dirichlet allocation and named entity recognition. In Presented at the IEEE international conference on computer communication and systems ICCCS14, presented at the proceedings of ieee international conference on computer communication and systems ICCCS14, Chennai, India. http://doi.org/10.1109/ICCCS.2014.7068182.
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: classification, clustering and extraction techniques. http://arxiv.org/abs/1707.02919. Accessed January 10, 2019.
An, L., Lin, X., Yu, C., & Zhang, X. (2015). Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes. Scientometrics, 105(3), 1605–1634. https://doi.org/10.1007/s11192-015-1640-4.
Bae, J.-H., Han, N.-G., & Song, M. (2014). Twitter issue tracking system by topic modeling techniques. Journal of Intelligence and Information Systems, 20(2), 109–122. https://doi.org/10.13088/jiis.2014.20.2.109.
Bansal, A., Sharma, V. K., Kumar, A., & Singh, M. (2005). DESIDOC bulletin of information technology: Success story with content coverage during 2000–2004. DESIDOC Journal of Library and Information Technology. https://doi.org/10.14429/djlit.25.4.3662.
Binkley, D., Heinz, D., Lawrie, D., & Overfelt, J. (2014). Understanding LDA in source code analysis. In Proceedings of the 22nd International Conference on Program Comprehension—ICPC 2014. Presented at the 22nd International Conference, Hyderabad, India (pp. 26–36). ACM Press. https://doi.org/10.1145/2597008.2597150.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. http://dl.acm.org/citation.cfm?id=944919.944937.
Chen, L.-C. (2017). An effective LDA-based time topic model to improve blog search performance. Information Processing and Management, 53(6), 1299–1319. https://doi.org/10.1016/j.ipm.2017.08.001.
Davarpanah, M. R., & Aslekia, S. (2008). A scientometric analysis of international LIS journals: Productivity and characteristics. Scientometrics, 77(1), 21–39. https://doi.org/10.1007/s11192-007-1803-z.
DESIDOC Journal of Library and Information Technology. (2016). http://publications.drdo.gov.in/ojs/index.php/djlit/index. Accessed January, 10 2019
Dora, M., & Kumar, A. H. (2017). An empirical analysis of the research trends in the field of library and information science in India—2004–2015. COLLNET Journal of Scientometrics and Information Management, 11(2), 361–378. https://doi.org/10.1080/09737766.2017.1317959.
Efron, M., Organisciak, P., & Fenlon, K. (2011). Building topic models in a federated digital library through selective document exclusion. Proceedings of the Association for Information Science and Technology, 48(1), 1–10.
Figuerola, C. G., García Marco, F. J., & Pinto, M. (2017). Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA. Scientometrics, 112(3), 1507–1535. https://doi.org/10.1007/s11192-017-2432-9.
Garg, K. C., & Sharma, C. (2017). Bibliometrics of library and information science research in india during 2004–2015. DESIDOC Journal of Library and Information Technology, 37(3), 221–227. https://doi.org/10.14429/djlit.37.3.11188.
George, C. P. (2015). Latent Dirichlet allocation: Hyperparameter selection and applications to electronic discovery. Gainesville: University of Florida.
Google Code Archive—Long-term storage for Google Code Project Hosting. Code.google.com. (2011a). https://code.google.com/archive/p/topic-modeling-tool/. Accessed January 10, 2019.
Google Code Archive—Long-term storage for Google Code Project Hosting. Code.google.com. (2011b). https://code.google.com/archive/p/topic-modeling-tool/wikis/TopicModelingTool.wiki. Accessed January10, 2019.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467–483. https://doi.org/10.1016/j.tourman.2016.09.009.
Kawalec, A. (2013). Research trends in library and information science based on Spanish scientific publication 2000 to 2010. Malaysian Journal of Library and Information Science, 18(2), 1–13.
Khan, I. (2016). A scientometric analysis of DESIDOC Journal of Library and Information Technology (2010–2014). Library Hi Tech News, 33(7), 8–12. https://doi.org/10.1108/LHTN-03-2016-0014.
Kim, S. G., & Kang, J. (2018). Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews. Information Processing and Management, 54(6), 938–957. https://doi.org/10.1016/j.ipm.2018.06.003.
Kumar, A., Bansal, A., & Kanungo, P. D. (2014). Unfolding the 33 years saga of DESIDOC Journal of Library and Information Technology. Annals of Library and Information Studies, 61(3), 203–211.
Lin, W.-Y. C. (2012). Research status and characteristics of library and information science in Taiwan: A bibliometric analysis. Scientometrics, 92(1), 7–21. https://doi.org/10.1007/s11192-012-0725-6.
Liu, L., Tang, L., Dong, W., Yao, S., & Zhou, W. (2016). An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1), 1608. https://doi.org/10.1186/s40064-016-3252-8.
Lu, K., & Wolfram, D. (2012). Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches. Journal of the American Society for Information Science and Technology, 63(10), 1973–1986.
Ma, R. (2012). Discovering and analyzing the intellectual structure and its evolution of LIS in China, 1998–2007. Scientometrics, 93(3), 645–659. https://doi.org/10.1007/s11192-012-0702-0.
Ma, T., Li, R., Ou, G., & Yue, M. (2018). Topic based research competitiveness evaluation. Scientometrics. https://doi.org/10.1007/s11192-018-2891-7.
Maity, B. K., & Hatua, S. R. (2015). Research trends of library management in LIS in India since 1950–2012. Scientometrics, 105(1), 337–346. https://doi.org/10.1007/s11192-015-1673-8.
MALLET-Topic Modeling. (2018). http://mallet.cs.umass.edu/topics.php. Accessed January 13, 2019.
Mehler, A., & Waltinger, U. (2009). Enhancing document modeling by means of open topic models: Crossing the frontier of classification schemes in digital libraries by example of the DDC. Library Hi Tech, 27(4), 520–539. https://doi.org/10.1108/07378830911007646.
Momtazi, S. (2018). Unsupervised latent Dirichlet allocation for supervised question classification. Information Processing and Management, 54(3), 380–393. https://doi.org/10.1016/j.ipm.2018.01.001.
Mukherjee, B. (2009). Scholarly research in LIS open access electronic journals: A bibliometric study. Scientometrics, 80(1), 167–194. https://doi.org/10.1007/s11192-008-2055-2.
Olmeda-Gómez, C., Ovalle-Perandones, M.-A., & Perianes-Rodríguez, A. (2017). Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014. Scientometrics, 113(1), 195–217. https://doi.org/10.1007/s11192-017-2486-8.
Pandita, R. (2014). DESIDOC Journal of Library and Information Technology (DJLIT): A Bibliometric study (2003–2012). Library Philosophy and Practice, 1038. http://digitalcommons.unl.edu/libphilprac/1038. Accessed January 10, 2019.
Ping, Q., & Chen, C. (2018). LitStoryTeller + : An interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox. Scientometrics, 116(3), 1887–1944. https://doi.org/10.1007/s11192-018-2803-x.
Sugimoto, C. R., Li, D., Russell, T. G., Finlay, S. C., & Ding, Y. (2011). The shifting sands of disciplinary development: Analyzing North American library and information science dissertations using latent Dirichlet allocation. Journal of the American Society for Information Science and Technology, 62(1), 185–204. https://doi.org/10.1002/asi.21435.
Sushma, H. R. (2018). DESIDOC journal of library and information technology (DJLIT): A bibliometric study. Information Studies, 5(1), 24–32.
Thavamani, K. (2013). Bibliometric Analysis of the DESIDOC journal of library and information technology for the year 2007–2011. International Journal of Information Dissemination and Technology, 3(1), 5.
Woltmann, S. L., & Alkærsig, L. (2018). Tracing university–industry knowledge transfer through a text mining approach. Scientometrics, 117(1), 449–472. https://doi.org/10.1007/s11192-018-2849-9.
Xu, H., Guo, T., Yue, Z., Ru, L., & Fang, S. (2016). Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series. Scientometrics, 106(2), 583–601. https://doi.org/10.1007/s11192-015-1792-2.
Zhang, Y., Chen, M., Huang, D., Wu, D., & Li, Y. (2017). iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization. Future Generation Computer Systems, 66, 30–35. https://doi.org/10.1016/j.future.2015.12.001.
Zhang, Y., Ma, J., Wang, Z., Chen, B., & Yu, Y. (2018). Collective topical PageRank: A model to evaluate the topic-dependent academic impact of scientific papers. Scientometrics, 114(3), 1345–1372. https://doi.org/10.1007/s11192-017-2626-1.
Zhao, D. (2010). Characteristics and impact of grant-funded research: A case study of the library and information science field. Scientometrics, 84(2), 293–306. https://doi.org/10.1007/s11192-010-0191-y.
Zhao, F., Zhu, Y., Jin, H., & Yang, L. T. (2016). A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Future Generation Computer Systems, 65, 196–206. https://doi.org/10.1016/j.future.2015.10.012.
Zhu, Y., Yan, E., & Song, M. (2016). Understanding the evolving academic landscape of library and information science through faculty hiring data. Scientometrics, 108(3), 1461–1478. https://doi.org/10.1007/s11192-016-2033-z.
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Lamba, M., Madhusudhan, M. Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study. Scientometrics 120, 477–505 (2019). https://doi.org/10.1007/s11192-019-03137-5