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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]

The present study extract, map and compare the lexical and semantic similarity of terms from author-provided keywords with machine extracted terms and topics from titles and abstracts of an inter-disciplinary field like 'digital humanities'. Author-provided terms (keywords) were first extracted and mapped through visualization software like Gephi and then these extracted terms were compared with terms extracted from title and abstract of the research articles through NLP based statistical modules. Also, the interdisciplinary of significant topics were measured through the Brillouin index. A set of 7483 articles downloaded from Scopus database on the domain of digital humanities and its associated fields were used for the purpose. We observed the researches on digital humanities are spread over a considerable number of concepts like 'Industry 4.0', 'topic modelling, 'open science'. Further, the machine algorithm-based extraction compared and identified a larger lexical similarity between these author-provided keywords and title-extracted keywords, rather than abstract-extracted keywords. Jaccard similarity of all author-keywords with machine extracted title keywords came 0.83 and SBERT BiEncoder_score was 0.7374. The top research areas extracted from titles, through unsupervised approach of term extraction resulted in topics like digital humanities approach, digital humanities visualization, indicating a strong connection to the discipline of digital humanities. The average interdisciplinarity index of top significant topics came between 1.217 and 1.284, with the highest index value for 'computational digital humanities'. As this study is based on real-world data, it is highly useful to understand how far machine algorithm-based text extraction can be helpful for information retrieval process. [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]