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We present a new task: the identification of the emotions transmitted in Italian opera arias at the verse level. This is a relevant problem for the organization of the vast repertoire of Italian Opera arias available and to enable further analyses by both musicologists and the lay public.

We shape the task as a multi-class supervised problem, considering six emotions: love, joy, admiration, anger, sadness, and fear. In order to address it, we manually-annotated an opera corpus with 2. 5k verses—which we release to the research community—and experimented with different classification models and representations. Our best-performing models reach macroaveraged F1 measures of~ 0.45, always considering character 3-grams representations. Such performance reflects the difficulty of the task at hand, partially caused by the size and nature of the corpus, which consists of relatively short verses written in 18thcentury Italian.