Mapping research trends in text analytics and natural language processing in nursing: a bibliometric analysis

Abstract

Aims: This study maps the intellectual landscape and emerging trends at the intersection of text mining, natural language processing (NLP), and nursing using Web of Science (WoS) data.Methods: A bibliometric analysis was conducted using data from the WoS Core Collection. Studies published between January 1995 and December 2024 were retrieved using a Boolean search query targeting terms related to text analytics and nursing. After excluding non-English documents and studies unrelated to nursing, 251 peer-reviewed articles were included. Bibliometric mapping and network visualizations were generated using VOSviewer software, focusing on co-authorship patterns, keyword co-occurrence, and citation impact. Results: Publication volume surged post-2019, coinciding with the COVID-19 pandemic. The United States was the leading contributor, with Computers Informatics Nursing as the top venue. Keyword analysis identified four thematic clusters: (1) pandemic-related digital surveillance, (2) machine learning for patient safety, (3) AI-supported nursing education, and (4) general qualitative practice. Citation patterns indicated high interest in sentiment analysis.Conclusion: The findings highlight the growing integration of text analytics into nursing science, reflecting technological advancements and a shift toward data-informed decision-making. The thematic structure uncovered in this study points to a growing reliance on computational tools to support nursing education, clinical care, and research. These results contribute to a broader understanding of how digital methodologies are reshaping nursing knowledge production and signal key areas for future exploration.

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