Evidence map›Paper›PMID 37693657›Full record

ArticleMethodsX2023

A method for analyzing text using VOSviewer.

Umar Ali Bukar, Md Shohel Sayeed, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Oluwatosin Ahmed Amodu, Raja Azlina Raja Mahmood

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 13 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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  14. Review
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  16. Global research trends in traditional and complementary medicine for cancer supportive care: a bibliometric analysis (2000-2025).Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Review
  17. Article
  18. A Decadal Bibliometric Analysis of Circulating Tumor DNA (ctDNA) Research in Lung Cancer (2012-2022).Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
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  20. Review

7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Umar Ali BukarCentre for Intelligent Cloud Computing (CICC), Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Md Shohel SayeedCentre for Intelligent Cloud Computing (CICC), Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Siti Fatimah Abdul RazakCentre for Intelligent Cloud Computing (CICC), Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Sumendra YogarayanCentre for Intelligent Cloud Computing (CICC), Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Oluwatosin Ahmed AmoduDepartment of Electrical, Electronics Systems Engineering, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor 43600, Malaysia.
Raja Azlina Raja MahmoodDepartment of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan 43400, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for technical support for data handling and visualization solutions has increased in tandem with the complexity of today's data and information, that is of multiple sources, huge in size and of different formats. This study focuses on handling and analyzing text-based data. Despite many available text analysis tools, there is a high demand among researchers for easy- to-use tools yet scalable and with incomparable visualization features. Of recent, there has been a significant focus on utilizing VOSviewer, an open-source software for bibliometric analysis. This software is able to analyze a significant amount of data and provide excellent network data mapping. However, there is a lack of existing work in evaluating this sophisticated tool for text analysis. Thus, this article explores the capability of VOSviewer and presents evidence-based implementation of this software for text analysis. Specifically, this study demonstrates the usage of VOSviewer to analyze text based on YouTube interviews related to ChatGPT. Hence, this study significantly contributes by processing textual data and producing visualization network maps that are different from bibliometric data. The study recognizes VOSviewer as a powerful tool for data visualization in mapping text data and illustrates the potential of this software for analyzing text networks in various fields. •The study illustrates how text analysis and visualization can be realized using VOSviewer, an open-source software mostly used for biblio- metric analysis.•The study presents the workflow indicating how the dataset can be prepared as input for VOSviewer for text analysis.•The study proves that VOSviewer is a powerful tool for data visualization and network mapping for any type of network data including transcripts from social media.

Indexed as

Analysis and visualization of text dataDatasetText analysisVisualizationVOSviewer

Identifiers

PMID37693657
PMCPMC10491643

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.