HJ-BIPLOT: A Theoretical and Empirical Systematic Review of Its 38 Years of History, Using Text Mining and LLMs

The HJ-Biplot, introduced by Galindo in 1986, is a multivariate analysis technique that enables the simultaneous representation of rows and columns with high-quality visualization. This systematic review synthesizes findings from 121 studies on the HJ-Biplot, spanning from 1986 to December 2024. Studies were sourced from Scopus, Web of Science, and other bibliographic repositories. This review aims to examine the theoretical advancements, methodological extensions, and diverse applications of the HJ-Biplot across disciplines. Text mining was performed using IRAMUTEQ software, and Canonical Biplot analysis was conducted to identify four key evolutionary periods of the technique. A total of 121 studies revealed that health (14.9%), sustainability (11.6%), and environmental sciences (12.4%) are the primary areas of application. Canonical Biplot analysis showed that two main dimensions explained 80.24% of the variability in the dataset with Group 4 (2016–2024) achieving the highest cumulative representation (98.1%). Recent innovations, such as the Sparse HJ-Biplot and Cenet HJ-Biplot, have been associated with contemporary topics like COVID-19, food security, and sustainability. Artificial intelligence (ChatGPT 3.5) enriched the analysis by generating a detailed timeline and identifying emerging trends. The findings highlight the HJ-Biplot’s adaptability in addressing complex problems with significant contributions to health, management, and socioeconomic studies. We recommend future research explore hybrid applications of the HJ-Biplot with machine learning and artificial intelligence to further enhance its analytical capabilities and address its current limitations.