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Mohit Garg,Uma Kanjilal 한국과학기술정보연구원 2022 Journal of Information Science Theory and Practice Vol.10 No.3
This paper aims to implement a topic modeling technique for extracting the topics of online discussions among library professionals in India. Topic modeling is the established text mining technique popularly used for modeling text data from Twitter, Facebook, Yelp, and other social media platforms. The present study modeled the online discussions of Library and Information Science (LIS) professionals posted on Lis Links. The text data of these posts was extracted using a program written in R using the package “rvest.” The data was pre-processed to remove blank posts, posts having text in non-English fonts, punctuation, URLs, emails, etc. Topic modeling with the Latent Dirichlet Allocation algorithm was applied to the pre-processed corpus to identify each topic associated with the posts. The frequency analysis of the occurrence of words in the text corpus was calculated. The results found that the most frequent words included: library, information, university, librarian, book, professional, science, research, paper, question, answer, and management. This shows that the LIS professionals actively discussed exams, research, and library operations on the forum of Lis Links. The study categorized the online discussions on Lis Links into ten topics, i.e. “LIS Recruitment,” “LIS Issues,” “Other Discussion,” “LIS Education,” “LIS Research,” “LIS Exams,” “General Information related to Library,” “LIS Admission,” “Library and Professional Activities,” and “Information Communication Technology (ICT).” It was found that the majority of the posts belonged to “LIS Exam,” followed by “Other Discussions” and “General Information related to the Library.”
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