Stairway to heaven: An emotional journey in Divina Commedia with threshold-based Naïve Bayes classifier

Computational literary uses data science and computer science techniques to study literature. In this framework, we investigate how an expert system can acquire knowledge from the specific content of a narrative text without any pre-existing information about it. We utilize the Threshold-based Na & iuml;ve Bayes (Tb-NB) classifier to analyze the content of Dante Alighieri's Divina Commedia poem. Tb-NB is a probabilistic data-driven model that predicts the polarity of a binary response based on the probability of an event occurring given certain features, and assigns a log-likelihood score to each word in a text. Our first task is understanding if and how the links between lexical forms and meanings characterize the three parts of the poem (Inferno, Purgatorio and Paradiso) in order to predict if a Canto belongs to Inferno or Paradiso based on its specific content, and to determine if a Canto of Purgatorio is more similar to those of Inferno or to those of Paradiso. We show Tb-NB outperform other similar approaches and achieves the same performance of Random Forest (F1-score = 0.985) but providing much more information to interpret the specific content and the lexical forms used by Dante Alighieri in its poem. The Tb-NB's scores are the base of knowledge for the implementation of an expert system, like a search engine, that can help users to identify the most informative verses of a Canto or by better comprehend or discover the content of the poem from a word related to a particular feeling or emotion.