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Identifying trends in research through co-citation or content analysis of journal contents is quite a common practice in LIS research. In this study, however, we proposed the Latent Dirichlet Allocation (LDA), a popular topic-modeling approach for identifying research trends of published articles in three scopus-indexed Indian LIS journals. A total of 1213 titles & their abstracts published between 2011 and 2022 have been considered. From these data, a corpus of frequently used 15 key phrases was identified from each journal using Count Vectorizer and then ten topics having higher coherence scores were extracted from each journal corpus using LDA techniques to understand to what extent these topics are different in these journals. The analysis of the study indicates that 'Library users' studies' especially in academic libraries; and 'bibliometric indicators for measuring research growth are a few common topics in these journals and, technological innovation; utilisation of electronic and print information resources; library management; or network analysis are some of the topics that are journal specific. From the t-SNE visualisation and pyLDAvis diagram, it was seen that the topics of DJLIT are significantly unique with discrete distributions than the other two journals. On analysing the growth of the top ten topics longitudinally, it was seen that research on digital libraries, analysing the global output, online search strategy, ranking universities, etc. are concurrent interests of research among researchers while academic library resources, including electronic resources and its use, open access are among diminishing research interests of authors. Since the topic-modeling approach can provide results devoid of bias, it can be used to identify research land scape longitudinally as well as obsolescence of topic in a domain. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to identify the research fronts by analysing highly cited core papers adjusted with the age of a paper in library and information science (LIS) where natural language processing (NLP) is being applied significantly. Design/methodology/approach: By excavating international databases, 3,087 core papers that received at least 5% of the total citations have been identified. By calculating the average mean years of these core papers, and total citations received, a CPT (citation/publication/time) value was calculated in all 20 fronts to understand how a front is relatively receiving greater attention among peers within a course of time. One theme article has been finally identified from each of these 20 fronts. Findings: Bidirectional encoder representations from transformers with CPT value 1.608 followed by sentiment analysis with CPT 1.292 received highest attention in NLP research. Columbia University New York, in terms of University, Journal of the American Medical Informatics Association, in terms of journals, USA followed by People Republic of China, in terms of country and Xu, H., University of Texas, in terms of author are the top in these fronts. It is identified that the NLP applications boost the performance of digital libraries and automated library systems in the digital environment. Practical implications: Any research fronts that are identified in the findings of this paper may be used as a base for researchers who intended to perform extensive research on NLP. Originality/value: To the best of the authors' knowledge, the methodology adopted in this paper is the first of its kind where meta-analysis approach has been used for understanding the research fronts in sub field like NLP for a broad domain like LIS. [ABSTRACT FROM AUTHOR]

Keyword extraction is the task of identifying important terms or phrase that are most representative of the source document. Although the process of automatic extraction of keywords from title is an old method, it was mainly for extraction from a single web document. Our approach differs from previous research works on keyword extraction in several aspects. For those who are non-expert of the scientific fields, understating scientific research trends is difficult. The purpose of this study is to develop an automatic method of obtaining overviews of a scientific field for non-experts by capturing research trends. This empirical study excavates significant term extraction using Natural Language Processing (NLP) tools. More than 15000 titles saved in a .csv file was our dataset and scripts written in Python were our process to compare how far significant terms of scientific title corpus are similar or different to the terms available in the abstract of that same scientific article corpus. A light-weight unsupervised title extractor, Yet Another Keyword Extractor (YAKE) was used to extract the results. Based on our analysis, it can be concluded that these algorithms can be used for other fields too by the non-experts of that subject field to perform automatic extraction of significant words and understanding trends. Our algorithm could be a solution to reduce the labour-intensive manual indexing process. [ABSTRACT FROM AUTHOR]