Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study

Lamba, Manika; Madhusudhan, Margam

doi:10.1007/s11192-019-03137-5

Cited by 29 publications

(12 citation statements)

References 34 publications

(33 reference statements)

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“…This paper applies topic modeling to identify the research trends in systems engineering over recent decades. Analysis of the topics may assist researchers in identifying new issues and concepts in a research field 18 . Researchers may perform manual or automated topic modeling to extract core topics from published research.…”

Section: Bibliometricsmentioning

confidence: 99%

Analysis of INCOSE Systems Engineering journal and international symposium research topics

Oosthuizen

Pretorius

2021

Systems Engineering

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The pressure on systems engineering is ever‐increasing to support the development and implementation of systems that meet a complex environment's demands. As a growing discipline, systems engineering requires insight into past research to identify opportunities for future growth. Analyzing the bibliometric data on published research provides valuable information on a scientific discipline's past progress and future prospects. Therefore, this paper extracts the research topics published in INCOSE's journal Systems Engineering and the annual international symposium proceedings to analyze their composition and allocation to papers. The implemented process applies natural language processing and topic modeling to extract the main topics from these papers' titles and abstracts. Analyzing these research topics' composition and mapping them to processed articles helps to understand their relative importance. The analysis's output confirms the importance of modeling in systems engineering, as it is the most popular topic. The additional focus of research papers on the systems engineering process, practice, and methodologies also indicates that the field is still growing and evolving. Some important topics to systems engineering, which were not found as prominent topics, are humans' roles in systems, verification and validation, and other specialty fields. This new knowledge about the structure of research into systems engineering can identify future research project opportunities to continue growing the field.

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Section: Bibliometricsmentioning

confidence: 99%

Analysis of INCOSE Systems Engineering journal and international symposium research topics

Oosthuizen

Pretorius

2021

Systems Engineering

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“…Topic modeling is one of the most practical and essential methods for analyzing coronavirus publications. This type of modeling acts as a text mining tool for processing, organizing, managing, and extracting knowledge, is commonly applied to identify basic “topics” in texts (Lamba and Madhusudhan, 2019), and provide a practical and effective representation of a vast collection of documents, publications, and the relationships between them (Jelodar et al , 2019).…”

Section: Discussionmentioning

confidence: 99%

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

Danesh

Dastani

Ghorbani

2021

LHT

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PurposeThe present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.Design/methodology/approachThe present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.FindingsThe findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”Originality/valueThe originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

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“…LDA's fundamental purpose is to compute the posterior of hidden variables given the observable variables' values. Articles with similar themes will employ similar groupings of words, ii) articles are a probability distribution over latent topics, and iii) topics are probability distributions over words [21] as shown in Figure 1. LDA is a corpus-based generative probabilistic model [2].…”

Section: Latent Dirichlet Allocation (Lda)mentioning

confidence: 99%

A text mining and topic modeling based bibliometric exploration of information science research

Silwattananusarn

Kulkanjanapiban

2022

IJ-AI

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This study investigates the evolution of information science research based on bibliometric analysis and semantic mining. The study discusses the value and application of metadata tagging and topic modeling. Forty-two thousand seven hundred thirty-eight articles were extracted from Clarivate Analytic's Web of Science Core Collection 2010-2020. This study was divided into two phases. Firstly, bibliometric analyzes were performed with VOSviewer. Secondly, the topic identification and evolution trends of information science research were conducted through the topic modeling approach latent dirichlet allocation (LDA) is often used to extract themes from a corpus, and the topic model was a representation of a collection of documents that is simplified using topic-modeling-toolkit (TMT). The top 10 core topics (tags) were information research design, information health-based, model data public, study information studies, analysis effect implications, knowledge support web, data research, social research study, study media information, and research impact time for the studied period. Not only does topic modeling assist in identifying popular topics or related areas within a researcher's area, but it may be used to discover emerging topics or areas of study throughout time.

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Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study

Cited by 29 publications

References 34 publications

Analysis of INCOSE Systems Engineering journal and international symposium research topics

Analysis of INCOSE Systems Engineering journal and international symposium research topics

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

A text mining and topic modeling based bibliometric exploration of information science research

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