MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction

Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues

To Sing like a Mockingbird.

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Un bianco geometrico: Lettura dall’Etica di Spinoza

The article collects the speech given at the Spinoza Seminar at the Fondazione Corrente, on January 17th, 2023, by the contemporary Italian-Belgian artist Lorenzo Gatti (1955), who over the years has created a series of large ‘maps’ dedicated to Baruch Spinoza’s work Ethica, ordine geometrico demonstrata (1677)

Sviluppo di approcci innovativi alla caratterizzazione di biomasse a fini energetici di seconda generazione

The thesis, according to the objects of the European project BRISK, is focused on the development of the approach to the characterization of biomass as II generation biofuels; that includes the qualification of innovative semi-industrial practices, the development of neural networks and the creation of a database