## 3 A New Seven-Phase Methodology for Systematic Literature Review
### Phase 6: Use of ChatGPT 3.5 Artificial Intelligence for Textual Analysis

Table 6: Some results obtained from the execution of prompts with ChatGPT 3.5. The integration of ChatGPT 3.5 into the systematic literature review on the HJ-Biplot proved to be a transformative approach, enabling a deeper and more nuanced understanding of the technique's evolution and applications. By leveraging AI-generated insights, this study not only identified key trends and seminal works but also uncovered patterns that traditional methods might have overlooked. The results presented in Table 6 represent only a portion of the extensive information extracted by ChatGPT 3.5, which includes detailed chronological analyses, key article identification, and the evolution of HJ-Biplot applications across various fields. However, these AI-generated insights must be carefully cross-referenced with the findings from the Canonical Biplot and text mining analyses, which were conducted as preliminary steps in this study. This triangulation of methods ensures the robustness and reliability of the conclusions, as it combines the strengths of AI-driven textual analysis with the precision of multivariate visualization and the depth of lexicometric exploration. This innovative methodology underscores the potential of combining advanced AI tools with traditional analytical techniques to enhance research outcomes, paving the way for future interdisciplinary studies that can address complex, multidimensional problems with greater precision and insight. Phase 7: Consolidation of Information and Creation of the Timeline of the HJ-Biplot Technique Development  
The HJ-Biplot, introduced by [PERSON] in 1986, was a significant advancement in multivariate analysis, enabling the simultaneous representation of data matrices with superior fitting for both rows and columns in the same reference system [12]. Following its introduction, the technique was applied in various fields. [PERSON] in [75] used it to define characteristics of large lymphocyte populations in B-CLL cases. In 1991, [PERSON] explored the spatial distribution of dung-feeding scarabs in Mediterranean pasturelands [76], while [PERSON] et al. differentiated young wines by anthocyanin profiles, showcasing the method's ability for classification and discrimination [77]. [PERSON] et al. (1993) expanded its use to classify wines geographically based on phenolic and chemical variables [78]. Additionally, [PERSON] et al. (1994) applied the HJ-Biplot to distinguish grape varieties by anthocyanin composition, demonstrating its robustness in agricultural and food sciences [79]. In 1996, [PERSON] advanced the application of the HJ-Biplot with his study _\"Comparative Study of the Ordering of Ecological Communities Based on Factorial Techniques\"_[80], demonstrating its effectiveness in analyzing ecological communities compared to other multivariate techniques. In 1999, [PERSON] applied the method to aquatic ecosystems in \"_HJ-Biplot analysis as a tool for studying an aquatic ecosystem_\" [81], emphasizing its utility in interpreting ecosystem indices, while [PERSON] et al. extended its use to geology, analyzing the origin and evolution of building stones based on chemical composition [82]. The versatility of the HJ-Biplot was further showcased in 2005 when [PERSON] et al. identified personality clusters among Chilean adolescents in _\"Personality Styles and Social Maladaptation During Adolescence\"_[83], and [PERSON] et al. monitored the geological evolution of building stones through chemical analysis [84]. In 2006, [PERSON] et al. employed the technique to study air pollution in Salamanca over a five-year period, capturing pollutant relationships and temporal evolution [85], while in 2007, [PERSON] and [PERSON] analyzed political polarization in Latin America using the HJ-Biplot to reveal ideological dimensions [86], and [PERSON] examined socioeconomic profiles of women in undeclared employment in Salamanca [87].