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About

Valentina Franzoni’s research lies at the intersection of affective computing, natural language processing, and semantic analysis, with a focus on how machines can understand and quantify human emotions. Her most influential contribution is the SEMO (Semantic Model for Emotion Recognition) framework, which detects and measures the emotional load of basic emotions in short, text-rich contexts such as news headlines, tweets, and captions. This work, with 26 citations, has become a foundational tool for sentiment analysis and emotion-aware AI. She further advanced this area with a web-based system for emotion vector extraction and explored the semantic recognition of emotional context through emoji pictogram classification. Beyond text, Franzoni has contributed to robotics, notably developing the autonomous hexapod robot gAItano, which integrates artificial vision and myoelectric gesture control. During the COVID-19 pandemic, she applied her expertise to study negative emotions as a means of preserving social distance, demonstrating the real-world relevance of her work. With a growing body of highly cited research, Franzoni is recognized for bridging semantic modeling and emotional intelligence, offering practical tools for understanding human affect in digital environments.