The correspondence of historical personalities serves as a rich source of psychological, social, and economic information. Letters were indeed used as means of communication within the family circles but also a primary method for exchanging information with colleagues, subordinates, and employers. A quantitative analysis of such material enables scholars to reconstruct both the internal psychology and the relational networks of historical figures, ultimately providing deeper insights into the socio-economic systems in which they were embedded. In this study, we analyze the outgoing correspondence of Michelangelo Buonarroti, a prominent Renaissance artist, using a collection of 523 letters as the basis for a structured text analysis. Our methodological approach compares three distinct Natural Language Processing Methods: an Augmented Dictionary Approach, which relies on static lexicon analysis and Latent Dirichlet Allocation (LDA) for topic modeling, a Supervised Machine Learning Approach that utilizes BERT-generated letter embeddings combined with a Random Forest classifier trained by the authors, and an Unsupervised Machine Learning Method. The comparison of these three methods, benchmarked to biographic knowledge, allows us to construct a robust understanding of Michelangelo's emotional association to monetary, thematic, and social factors. Furthermore, it highlights how the Supervised Machine Learning method, by incorporating the authors' domain knowledge and understanding of documents and background, can provide, in the context of Renaissance multi-themed letters, a more nuanced interpretation of contextual meanings, enabling the detection of subtle (positive or negative) sentimental variations due to a variety of factors that other methods can overlook.
