Libraries at University of Nebraska-Lincoln

 

Citation

Abinaya, G. and Winster, S.G. (2014), “Event identification in social media through latent dirichlet allocation and named entity recognition”, in: Proceedings of IEEE International Conference on Computer Communication and Systems ICCCS14. pp 142–146

Bae, J-H. Han, N-G., and Song, M. (2014), “Twitter Issue Tracking System by Topic Modeling Techniques”, Journal of Intelligence and Information Systems, Vol. 20, pp. 109–122. doi: 10.13088/jiis.2014.20.2.109

Bapte, V.D. (2017), “DESIDOC Journal of Library and Information Technology (DJLIT): A Bibliometric Analysis of Cited References”, DESIDOC Journal of Library & Information Technology, Vol. 37, pp. 264–269.

Blei, D.M. and Lafferty, J.D. (2006), “Dynamic Topic Models”, in: Proceedings of the 23rd International Conference on Machine Learning. ACM, New York, NY, USA, pp 113–120.

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

Cagliero, L., Garza, P., Kavoosifar, M.R., Baralis, E. (2018), “Discovering cross-topic collaborations among researchers by exploiting weighted association rules”, Scientometrics, Vol. 116, pp. 1273–1301. doi: 10.1007/s11192-018-2737-3

Chen, J., Wang, T.T., and Lu, Q. (2016), “THC-DAT: a document analysis tool based on topic hierarchy and context information”, Library Hi Tech, Vol. 34, pp. 64–86. doi: 10.1108/LHT-07-2015-0074

Chen, L-C. (2017), “An effective LDA-based time topic model to improve blog search performance”, Information Processing & Management, Vol. 53, pp. 1299–1319. doi: 10.1016/j.ipm.2017.08.001

DESIDOC Journal of Library & Information Technology. http://publications.drdo.gov.in/ojs/index.php/djlit/index. Accessed 30 Oct 2018

Efron, M., Organisciak, P., and Fenlon, K. (2011), “Building topic models in a federated digital library through selective document exclusion”, Proceedings of the Association for Information Science and Technology, Vol. 48, pp. 1–10.

Figuerola, C.G., García, Marco F.J., and Pinto, M. (2017), “Mapping the evolution of library and information science (1978–2014) using topic modeling on LISA”, Scientometrics, Vol. 112, pp. 1507–1535. doi: 10.1007/s11192-017-2432-9

Garg, K.C., and Sharma, C. (2017), “Bibliometrics of Library and Information Science research in India during 2004-2015”, DESIDOC JOURNAL OF LIBRARY & INFORMATION TECHNOLOGY, Vol. 37, pp. 221–227. doi: 10.14429/djlit.37.3.11188

Google Code Archive - Long-term storage for Google Code Project Hosting. (2011a) https://code.google.com/archive/p/topic-modeling-tool/. Accessed 10 Oct 2018

Google Code Archive - Long-term storage for Google Code Project Hosting. (2011b) https://code.google.com/archive/p/topic-modeling-tool/wikis/TopicModelingTool.wiki.Accessed 10 Oct 2018

Guo, Y., Barnes, S.J., and Jia, Q. (2017), “Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation”, Tourism Management, Vol. 59, pp. 467–483. doi: 10.1016/j.tourman.2016.09.009

Hu, Z., Fang, S., and Liang, T. (2014), “Empirical study of constructing a knowledge organization system of patent documents using topic modeling”, Scientometrics, Vol.100, pp. 787–799. doi: 10.1007/s11192-014-1328-1

Katsurai, M., Ohmukai, I., and Takeda, H. (2016), “Topic Representation of Researchers’ Interests in a Large-Scale Academic Database and Its Application to Author Disambiguation”, IEICE Trans Inf & Syst E99.D:1010–1018. doi: 10.1587/transinf.2015DAP0031

Kim, S.G., and Kang, J. (2018), “Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews”, Information Processing & Management, Vol. 54, pp. 938–957. doi: 10.1016/j.ipm.2018.06.003

Koltsova, O., Koltcov, S. (2013), “Mapping the public agenda with topic modeling: The case of the Russian livejournal”, Policy & Internet, Vol. 5, pp. 207–227.

Lee, J., Lapira, E., Bagheri, B., and Kao, H. (2013), “Recent advances and trends in predictive manufacturing systems in big data environment”, Manufacturing Letters, Vol. 1, pp. 38–41. doi: 10.1016/j.mfglet.2013.09.005

Lin, L., Xu, Z., Ding, Y., and Liu, X. (2013), “Finding topic-level experts in scholarly networks”, Scientometrics, Vol. 97, pp. 797–819. doi: 10.1007/s11192-013-0988-6

Liu, L., et al. (2016), “An overview of topic modeling and its current applications in bioinformatics”, doi: 10.1186/s40064-016-3252-8

Lu, K., and 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, Vol. 63, pp. 1973–1986. doi: 10.1002/asi.22628

Lu, K., Cai, X., Ajiferuke, I., and Wolfram, D. (2017), “Vocabulary size and its effect on topic representation”, Information Processing & Management, Vol. 53, pp. 653–665. doi: 10.1016/j.ipm.2017.01.003

Ma, T., Li, R., Ou, G., and Yue, M. (2018), “Topic based research competitiveness evaluation”, Scientometrics. doi: 10.1007/s11192-018-2891-7

Mao, J., Cao, Y., Lu, K., and Li, G. (2017), “Topic scientific community in science: a combined perspective of scientific collaboration and topics”, Scientometrics, Vol. 112, pp. 851–875. doi: 10.1007/s11192-017-2418-7

Mehler, A., and 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, Vol. 27, pp. 520–539. doi: 10.1108/07378830911007646

Momtazi, S. (2018), “Unsupervised Latent Dirichlet Allocation for supervised question classification”, Information Processing & Management, Vol. 54, pp. 380–393. doi: 10.1016/j.ipm.2018.01.001

Nichols, L.G. (2014), “A topic model approach to measuring interdisciplinarity at the National Science Foundation”, Scientometrics, Vol. 100, pp. 741–754. doi: 10.1007/s11192-014-1319-2

Noh, Y. (2015), ‘Imagining Library 4.0: Creating a Model for Future Libraries”, The Journal of Academic Librarianship, Vol. 41, pp. 786–797. doi: 10.1016/j.acalib.2015.08.020

Rosen-Zvi, M., et al. (2010), “Learning author-topic models from text corpora”, ACM Transactions on Information Systems, Vol. 28, pp. 1–38. doi: 10.1145/1658377.1658381

Sugimoto, C.R., et al. (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, Vol. 62, pp. 185–204. doi: 10.1002/asi.21435

Wang, J., et al. (2013), “Unsupervised mining of long time series based on latent topic model”, Neurocomputing, Vol. 103, pp. 93–103. doi: 10.1016/j.neucom.2012.09.008

Yan, J., et al. (2017), “Towards big topic modeling”, Information Sciences, Vol. 390, pp. 15–31. doi: 10.1016/j.ins.2016.12.014

Zhang, Y., et al. (2017), “iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization”, Future Generation Computer Systems, Vol. 66, pp. 30–35. doi: 10.1016/j.future.2015.12.001

Zhang Y., et al. (2018), “Collective topical PageRank: a model to evaluate the topic-dependent academic impact of scientific papers”, Scientometrics, Vol. 114, pp. 1345–1372. doi: 10.1007/s11192-017-2626-1

Zhao, F., Zhu, Y., Jin, H., and Yang, L.T. (2016), “A personalized hashtag recommendation approach using LDA-based topic model in microblog environment”, Future Generation Computer Systems, Vol. 65, pp. 196–206. doi: 10.1016/j.future.2015.10.012

Abstract

This study presents a method to analyze textual data and applying it to the field of Library and Information Science. This paper subsumes a special case of Latent Dirichlet Allocation and Author-Topic models where each article has one unique author and each author has one unique topic. Topic Modeling Toolkit is used to perform the author-topic modeling. The study further which considers topics and their changes over time by taking into account both the word co-occurrence pattern and time. 393 full-text articles were downloaded from DESIDOC Journal of Library and Information Technology and were analyzed accordingly. 16 core topics have been identified throughout the period of ten years. These core topics can be considered as the core area of research in the journal from 2008 to 2017. This paper further identifies top five authors associated with the representative articles for each studied year. These authors can be treated as the subject-experts for the modeled topics as indicated. The results of the study can serve as a platform to determine the research trend; core areas of research; and the subject-experts related to those core areas in the field the Library and Information Science in India.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.