APPLICATION OF TOPIC MODELING AND DATA VISUALIZATION IN LIBRARIES: A REVIEW OF LITERATURE 10 5

Abstract

In response to the exponential growth of information, libraries are increasingly adopting advanced computational approaches such as topic modelling, text mining, authorship network analysis, and data visualization. These emerging techniques empower users to access precise information while enhancing the efficiency of library services. This study explores the evolving role of topic modelling and data visualization within Library and Information Science (LIS), focusing on their theoretical underpinnings, practical applications, and recent research trends.

A review of 73 scholarly publications from 2003 to 2024 was conducted using Scopus, Web of Science, and Google Scholar databases. The findings reveal that 75% of the literature emerged in the past decade, with a notable concentration between 2018 and 2022, indicating heightened academic interest in these data-driven methodologies. The reviewed documents include journal articles (70%), conference papers (18%), book chapters (9%), and theses (3%), highlighting diverse scholarly contributions.

Topic modelling techniques, particularly Latent Dirichlet Allocation (LDA), are recognized for uncovering hidden patterns in large text corpora, while data visualization translates complex datasets into impactful visuals. Together, these tools offer immense value to LIS professionals, data analysts, and researchers seeking to advance knowledge discovery, improve decision-making, and enhance scholarly communication in modern libraries.