LEVERAGING AI FOR ENHANCED BIBLIOMETRIC: A MULTI-FACETED APPROACH TO CITATION CONTEXT, SENTIMENT, AND COGNITIVE LINK ANALYSIS IN RESEARCH EVALUATION

Abstract

This study developed a comprehensive bibliometric framework by integrating data from Scopus, Web of Science, and Google Scholar, leveraging the unique strengths of each platform. The dataset, comprising 6,000 papers, was proportionally distributed across the three sources, with 40% from Scopus and 30% each from Web of Science and Google Scholar. Scopus provided curated, peer-reviewed content ideal for assessing high-impact scholarly works, while Web of Science complemented this with its long standing indexing of high-quality journals and conference proceedings. Google Scholar, with its broader scope, incorporated grey literature and interdisciplinary studies, addressing gaps left by traditional databases and ensuring a more inclusive dataset. The data collection process prioritized diversity and methodological rigor, combining the reliability of structured bibliographic sources with the inclusivity of non-traditional content. Advanced analytical techniques sentiment analysis, citation context classification, and citation network mapping were employed to uncover trends in research impact, intellectual linkages, and interdisciplinary collaborations. The results revealed that SCIE and SSCI indexed journals dominated the dataset, reflecting their established roles in disseminating high-quality research. Contributions from ESCI and open-access platforms showcased emerging and innovative avenues for bibliometric analysis. Geographically, the United States, United Kingdom, and China emerged as leading contributors, collectively accounting for over 65% of the analyzed research, highlighted their influence on global scholarly output. The study concluded that integrating multiple bibliometric databases minimizes potential biases, enhances the inclusivity of sources, and provides a more balanced evaluation of global research trends. To advance bibliometric methodologies further, future research should incorporate additional data sources and leverage advanced AI techniques to refine analytical processes and deepen insights into scholarly communication.