## 4 Discussion  
The HJ-Biplot has proven to be a powerful and versatile tool in the analysis of multivariate data with notable applications in fields such as health, sustainability, and socioeconomic studies. In the field of health, for example, its ability to identify complex patterns and relationships has enabled significant advances in the identification of risk profiles and the design of personalized interventions. A notable study is [106], which used the HJ-Biplot to analyze lifestyle clusters associated with cardiometabolic risks, providing valuable insights for the prevention of chronic diseases. In the field of sustainability, the HJ-Biplot has been employed in various applications that analyze environmental, economic, and social indicators. For example, in [104], this technique was applied to evaluate the _Sustainable Society Index_, providing a clear visual representation of countries with the best performance in sustainability. In [110], the HJ-Biplot was used to analyze the relationship between corporate social responsibility and financial performance in European companies, highlighting how sustainable practices can positively influence economic outcomes. Another relevant example is [117], where the HJ-Biplot was applied to assess the impact of environmental policies on greenhouse gas emissions reduction in different regions of the world, identifying the most effective strategies to combat climate change. These studies demonstrate the versatility of the HJ-Biplot in addressing complex problems in the field of sustainability, providing valuable insights for decision making in public policies and corporate strategies. In socioeconomic studies, the HJ-Biplot has been used to analyze survey data and employment profiles, as in the study [87] on women in irregular employment situations in Salamanca. However, its application in fields such as engineering and technology is less common, despite its potential to address complex problems in these areas. For example, the HJ-Biplot could be used to analyze sensor data in autonomous vehicles or to optimize processes in smart mining, where the identification of patterns and relationships could improve efficiency and decision making. These applications represent opportunities to expand the use of the HJ-Biplot in interdisciplinary and emerging contexts. An innovative aspect of this work is the combination of text mining techniques and artificial intelligence to analyze large volumes of information. By using tools such as IRAMUTEQ and ChatGPT 3.5, key insights were extracted and synthesized from 121 studies, identifying emerging trends and application areas that might have gone unnoticed with traditional methods. For example, the context provided by ChatGPT 3.5 enabled the generation of a detailed timeline summarizing the evolution of the HJ-Biplot from its introduction in 1986 to the most recent innovations, such as the Sparse HJ-Biplot and the Cenet HJ-Biplot. Additionally, text mining facilitated the identification of thematic patterns in the abstracts of the articles, enriching the analysis and providing a more comprehensive view of the contributions and limitations of the technique. This combination of methods not only enhanced the context of the information obtained but also demonstrated the potential of artificial intelligence to complement and enhance data analysis in systematic reviews. Despite its wide applicability, the HJ-Biplot faces several challenges that limit its use in certain contexts. One of the main challenges is the integration of heterogeneous data, such as the combination of genomic and clinical data in health studies. Although extensions like the Sparse HJ-Biplot have proven useful for dimensionality reduction in these cases, their interpretation remains complex and requires validation by experts in the field. Another challenge is scalability in large datasets, where techniques such as deep learning could complement the HJ-Biplot to improve the efficiency and accuracy of the analysis.