Giulio Biondi

University of Perugia, University of Florence

Papers

Total Citations

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About

Giulio Biondi’s research lies at the intersection of natural language processing, affective computing, and semantic analysis, with a focus on computationally modeling human emotion from text. His most influential contribution is the development of **SEMO**, a semantic model for emotion recognition that quantifies the emotional load of short, emotionally charged sentences such as news headlines, tweets, and captions. By leveraging semantic similarity between concepts, SEMO enables the detection of basic emotions—like joy, anger, or sadness—embedded in language, offering a nuanced alternative to keyword-based approaches. This work, published in 2017, has garnered **26 citations** and laid the foundation for his subsequent system for emotion vector extraction. Biondi also applied his expertise to socially relevant problems, such as analyzing negative emotions to support social distancing during the COVID-19 pandemic. His research demonstrates a commitment to making emotion recognition both interpretable and actionable, bridging computational linguistics and psychological theory. With a growing citation footprint, Biondi’s work is a valuable resource for researchers exploring sentiment analysis, human-computer interaction, and the semantic underpinnings of affect.