[논문]An Exploratory Analysis of Online Discussion of Library and Information Science Professionals in India using Text Mining

Garg, Mohit; Kanjilal, Uma

doi:10.1633/jistap.2022.10.3.4

An Exploratory Analysis of Online Discussion of Library and Information Science Professionals in India using Text Mining 원문보기

Journal of information science theory and practice : JISTaP, v.10 no.3, 2022년, pp.40 - 56

Garg, Mohit (Central Library, Indian Institute of Technology Delhi, New Delhi, India School of Social Science, Indira Gandhi National Open University) , Kanjilal, Uma (School of Social Science, Indira Gandhi National Open University)

Abstract ▼ AI-Helper

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."

주제어

표/그림 (15)

그림 Fig. 1. Methodology of data analysis.
표 Table 1. Sample dataset
그림 Fig. 3. Blank post.
그림 Fig. 2. Example of post containing image and extracted blank.
그림 Fig. 4. Regional language post.
표 Table 2. Extracted data of regional language
표 Table 3. Document term matrix ex ample
그림 Fig. 5. Wordcloud of frequent words in discussions on Lis Links.
그림 Fig. 6. Twenty-fivemostfrequentm words.
그림 Fig. 7. Comparative Wordcloud of words in each ten topics.
표 Table 4. Word wise beta score
표 Table 5. Gamma score of post per topic
표 Table 6. List of topic word and new category
표 Table 7. Classification of posts as per revised category
표 Table 8. Distribution of post as per revised category

AI 본문요약
AI-Helper

대상 데이터

A total of 10,286 posts were extracted using the crawler from Lis Links. The present study’s analysis is focused on the text dataset, so it is essential to harvest only text con- tent.
The extracted data was stored in CSV format. The extracted dataset consists of 10 attributes, namely PostID, PostUser, PostDate, PostTime, PostCategory, PostTitle, PostBody, PostView, PostReplyCount, and PostUrl. How- ever, the goal of this study will suffice with the analysis of text data of PostBody.

성능/효과

In addition to the number of contributions, this study has a few limitations. First, the present study only focused on LIS professionals registered with Lis Links online discussion forums; this could be a case of a statistically biased sample (Barbierato et al., 2022).
Fourth, the LDA topic model is based on the bag of words model, where semantic context is ignored. Lastly, we have assumed the value of k (number of topics) to be 10, based on the themes created by the previous studies, which resulted in a general category of topics.
Fourth, the LDA topic model is based on the bag of words model, where semantic context is ignored. Lastly, we have assumed the value of k (number of topics) to be 10, based on the themes created by the previous studies, which resulted in a general category of topics. The study with high value of k will generate more specific topics.
The results revealed that most discussions were tagged under the topic of “LIS Exams.” This showed the most significant interest of the LIS professionals in virtually sharing Information related to various jobs exams like KVS and NVS, and professional examinations like NET/SET, with the LIS community.

후속연구

Future research could compare these study results with the analysis of discussion on other community-based and social media platforms. As discussed in the limitations, future research can also be taken up to analyze these images and PDFs through image processing to present the granularity of the results. Further exploration of LDA on other Indian LIS forums is needed for a deeper understanding of the categories of the posts.
As discussed in the limitations, future research can also be taken up to analyze these images and PDFs through image processing to present the granularity of the results. Further exploration of LDA on other Indian LIS forums is needed for a deeper understanding of the categories of the posts. The combination of high and low value of k may be used to identify both general categories and their specific sub- categories.
Earlier studies on the forum or the email list used the manual content analysis methodology for classifying topics. Future research could compare these study results with the analysis of discussion on other community-based and social media platforms. As discussed in the limitations, future research can also be taken up to analyze these images and PDFs through image processing to present the granularity of the results.

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동의어: Packet Collision Rate

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