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English. The paper describes the ongoing experience at the University of Turin in developing linguistic resources and tools for sentiment analysis of social media. We describe in particular the development of Senti-TUT, a human annotated corpus of Italian Tweets including labels for sentiment polarity and irony, which has been recently exploited within the SENTIment POLarity Classification shared task at Evalita 2014. Furthermore, we report about our ongoing work on the Felicittà web-based platform for estimating happiness in Italian cities, which provides visualization techniques to interactively explore the results of sentiment analysis performed over Italian geotagged Tweets. Italiano. L’articolo presenta l’esperienza fatta presso l’Università di Torino nello sviluppo di risorse linguistiche e strumenti per la sentiment analysis di social media. In particolare, viene descritto Senti-TUT, un corpus di Tweet in Italiano, che include annotazioni relative alla polarità del sentiment e alla presenza di ironia, utilizzato nell’ambito del task di SENTIment POLarity Classification di Evalita 2014. Inoltre viene presentato il lavoro su Felicittà, una piattaforma Web per la stima della felicità nelle città italiane, che fornisce diverse modalità di visualizzazione del grado di felicità che emerge da un’analisi del sentiment su messaggi Twitter geolocalizzati in

Developing corpora and tools for sentiment analysis: the experience of the University of Turin group