Description: The Holistic Multimodal Framework is a comprehensive approach that combines various modalities of data and analysis methods to provide a more complete and rich understanding of complex phenomena. This framework is based on the premise that integrating different types of information, such as text, images, audio, and structured data, allows for a deeper and more accurate interpretation of the contexts in which they are applied. The main characteristics of this framework include its ability to handle heterogeneous data, its flexibility in applying analytical techniques, and its focus on the interconnection of different information sources. The relevance of the Holistic Multimodal Framework lies in its potential to address problems that require a multidimensional view, such as sentiment analysis on social media, pattern detection in large volumes of data, and enhancing human-computer interaction. By integrating multiple modalities, this framework not only enriches the analysis but also facilitates the generation of more robust and applicable insights across various disciplines, from artificial intelligence to social sciences.
