AriEmozione: Identifying Emotions in Opera Verses
Authors: Francesco Fernicola, Shibingfeng Zhang, Federico Garcea, Paolo Bonora, Alberto Barrón-Cedeño (Università di Bologna, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
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 F_1 measures of \sim0.45, 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.

Introduction
Opera lyrics have the function of expressing the emotional state of the singing character. In 17th- and 18th-century operas, characters brought on stage passions induced in their souls by the succession of events in the drama. Musicological studies use these affects as one of the interpretative keys of the work as a whole (Zoppelli, 2001; McClary, 2012). Being able to automatically identify the emotions expressed by the different arias of each work would provide scholars with a useful tool for a systematic study of the repertoire. The technology to identify the emotion(s) expressed by an aria represents an effective tool to study the vast repertoire of arias and characters of this period for musicologists and the lay public alike. As an aria may express more than one emotion, we go one granularity level lower —at the verse level. The task is defined as follows: Identify the emotion expressed in a verse, in the context of an aria. In order to do that we created the AriEmozione 1.0 corpus: a collection of 678 operas with 2.5k verses, each of which has been manually annotated with respect to emotion. We experimented with different supervised models (e.g., SVMs, neural networks) and text (e.g., character n-grams and distributed representations). Our experiments show that, regardless of the model, character 3-grams outperform all other representations, reaching weighted macro-averaged F1 measures of ∼0.45. Underrepresented classes (e.g., fear) are the hardest to identify. Others, such as anger and sadness, being both negative, are often confused between each other. The rest of the contribution is distributed as follows. Section 2 describes the AriEmozione 1.0 corpus. Section 3 describe the explored models and representations. Section 4 discusses the experiments and obtained results. Section 5 overviews some related work. Section 6 closes with conclusions and proposals for future work.

Conclusion
We addressed the novel problem of emotion classification of opera arias at the verse level. The task is interesting because of the lack of automated tools for the analysis of operas and challenging due to both the language used in 17th- and 18th-century lyrics and the complexity to produce the necessary amount of quality supervised data. We explored with various classification models and representations. A neural network with two hidden layers fed with a simple TF-IDF character 3-gram representation is among the most promising approaches to the problem. Among the six possible emotions, the most difficult to identify are rabbia and tristezza, which tend to be confused with each other, followed by ammirazione, which is often confused by gioia. In order to foster the research on this topic, we release the AriEmozione 1.0 corpus to the community (cf. footnote 2). As for the future work, we intend to increase the size of the AriEmozione 1.0 corpus by means of active learning (Yang et al., 2009). Once a larger data volume is produced, we plan to explore with models to identify the emotion at the aria rather than at the verse level. Following the theory of emotion proposed by Plutchik (1980), we could identify the emotion of a whole aria by combining the emotions at the verse level, and then conduct experiments to verify which granularity is more adequate as a single emotion unit. In order to address the issue of emotional polysemy and ambiguity of aria verses, we aim at producing explainable models by highlighting the specific fragments expressing the emotion. Another interesting alternative is the one highlighted by Zhao and Ma (2019), who adopted an efficient meta-learning approach to augment the learning ability of emotion distribution; i.e. the intensity values of a set of emotions within a single sentence, when the training dataset is small, as in the AriEmozione 1.0 corpus.