ChatGPT as an Artificial Intelligence Tool to Provide an Analytical Boost to Text Mining: An Application to TUCKER Models for Multiway Tables

In this comprehensive study, we meticulously investigated multidimensional data analysis techniques, particularly focusing on Tucker decomposition methods, spanning the period from 2000 to 2025. Our primary objective was to discern trends, advancements, and applications of these techniques across various domains of knowledge and how they have evolved over time. An extensive corpus of 288 scientific articles related to tensor decompositions, Tucker models and applications was previously reviewed. Multivariate methods such as text mining using IraMuteq software and MANOVA-Biplot were employed to visualize identified data patterns, and the analytical capability of ChatGPT artificial intelligence was assessed to provide contextual insights and add another layer of information to the research. Our conclusions underscore the importance of blending traditional statistical approaches with natural language processing prowess to achieve a profound understanding of the data. This analysis offers a comprehensive perspective on the evolution and application of multidimensional data analysis techniques, with a special emphasis on the enduring relevance of Tucker techniques in this new millennium.