Knowledge structure transition in library and information science: topic modeling and visualization
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DOI: 10.1007/s11192-020-03657-5
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- Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
- Staša Milojević & Cassidy R. Sugimoto & Erjia Yan & Ying Ding, 2011. "The cognitive structure of Library and Information Science: Analysis of article title words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(10), pages 1933-1953, October.
- Kun Lu & Dietmar Wolfram, 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, Association for Information Science & Technology, vol. 63(10), pages 1973-1986, October.
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- Pertti Vakkari & Yu-Wei Chang & Kalervo Järvelin, 2022. "Largest contribution to LIS by external disciplines as measured by the characteristics of research articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4499-4522, August.
- Abhijit Thakuria & Dipen Deka, 2024. "A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3841-3869, July.
- Manuel A. Vázquez & Jorge Pereira-Delgado & Jesús Cid-Sueiro & Jerónimo Arenas-García, 2022. "Validation of scientific topic models using graph analysis and corpus metadata," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5441-5458, September.
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Keywords
Library and information science; Latent Dirichlet allocation; Topic modeling; Visualization; Research trend;All these keywords.
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