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3 septembre 2021
- doi: 10.4000/books.aaccademia.8528
- eisbn: 9791280136336
https://www.openedition.org/12554 , info:eu-repo/semantics/openAccess
Mots-clés
Computational Linguistics Fine-grained sentiment analysis Distributional Semantics Quantitative Linguistic Investigations Gender Bias Depression from Social Media Online Hate Speech Automatic Sarcasm Detection TrAVaSI AriEmozione AEREST COVID-19 Linguistic Ostracism in Social Networks Multilingual NLU E3C Project DistilBERT Twitter during Pandemic LAN000000 Linguistics CBXSujets proches
Operas--History and criticism Opera, Comic Opera--History and criticism Comic opera Lyric drama Human emotions Passions FeelingsCiter ce document
Francesco Fernicola et al., « AriEmozione: Identifying Emotions in Opera Verses », Accademia University Press, ID : 10.4000/books.aaccademia.8528
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Résumé
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 macro-averaged 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 18th-century Italian.