Annotation and Detection of Emotion Polarity in I Promessi Sposi: Dataset and Experiments
Authors: Rachele Sprugnoli, Arianna Redaelli (Università di Parma, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Emotions play a crucial role in literature and are studied by various disciplines, e.g. literary criticism, psychology, anthropology and, more recently, also with computational methods in NLP. However, studies in the Italian context are still limited. This work therefore aims to advance the state of the art in the field of emotion analysis applied to historical texts by proposing a new dataset and describing the results of a set of emotion polarity detection experiments. The text analyzed is ``I Promessi Sposi'' in its final edition (published in 1840), one of the most important novels in the Italian literary and linguistic canon.

Introduction
Emotions play a key role in literature, representing a bridge between the author's purposes, the text, and the reader's personal background: literature collects experiences and contains the emotions that accompany them, in turn generating new experiences and new emotions. Therefore, studying emotions in literary texts implies the possibility of providing valuable insights into the deeper meanings and intentions behind a work, the form it may take, and the readers' engagement with it. This field of study has recently experienced a flourishing national and international development involving different disciplines, from literary criticism to philosophy, from anthropology to psychology. For example, in the Italian context, Ginzburg et al. AUTHOR analyzed how Matte Blanco's psychoanalytic theories on emotions are applied to literary criticism, taking into account authors like Tozzi, Pirandello, and Svevo, while Guaragnella AUTHOR explored the complex interaction between humor and sadness in 20th-century Italian literature from a both philosophical and literary point of view. However, some literary works remained under-explored. One such work is Alessandro Manzoni's ``I Promessi Sposi''. Despite its emotional richness, the novel has often been regarded as monolithic and static, both because of the narrated events, strongly influenced by the author's religious spirit and social and political polemic, and because it quickly became a model of the Italian language, stably included in school curricula as mandatory study material. This has led to a certain degree of reluctance and lack of enthusiasm among the readers. As a consequence, a study of emotions in ``I Promessi Sposi'' can be beneficial from both an academic and educational standpoint. Academically, it can provide new insights into a classic text, encouraging new interpretations and scholarly discussions. For didactic purposes, analyzing the emotions in ``I Promessi Sposi'' can make the novel more relatable and appealing for students, revealing the depth and complexity of the characters' experiences in the context in which they live, and encouraging a closer connection with them and with Manzoni's social issues. Given this context, computational methods, already widely applied especially on user-generated contents (such as reviews and social media posts), can be profitably tested on the fictional text after developing specific datasets for training and evaluating new models. The present work takes as a basis a preliminary annotation of the Manzoni's novel, expanding the number of manually labeled sentences and proposing the development of some models of varying complexity. More specifically, two are the main contributions of our work: i) we release} a new dataset made of more than 3.000 sentences taken from ``I Promessi Sposi'' manually annotated with four emotion polarity classes (i.e. POSITIVE, NEGATIVE, NEUTRAL, MIXED); ii) we test various approaches for emotion polarity detection using the new dataset as-is but also augmenting it with other annotated Italian resources.

Conclusion
This paper presents a new manually annotated dataset and a set of experiments for the automatic detection of emotion polarity. More specifically, the dataset contains 3,095 sentences taken from ``I Promessi Sposi'' and the experiments cover different approaches, namely lexicon-based, SVC and the fine-tuning of an Italian BERT model and of the multilingual XLM-RoBERTa model. The impact of the training set size is also evaluated by increasing the in-domain dataset by combining other annotated Italian resources. We are aware that for the emotion analysis task, as for all NLP tasks, Large Language Models are now widely used AUTHOR but these require computational powers currently not available to the authors of the paper. In the future, our work will focus on this aspect in order to be in line with the current state of the art. Another future work will concern the annotation of emotions with more granular labels, extending an activity already started on Chapter VIII only, on which the label scheme proposed for the GoEmotions dataset AUTHOR was applied AUTHOR. Additionally, we plan to pay greater attention to the annotation of irony, a crucial aspect of the novel. This could be incorporated into the dataset using a binary 0/1 value to indicate its presence or absence, as we have already begun to implement }. Finally, we would like to explore the applications of our work in the school context. Concerning the study of emotions in Manzoni's novel, computational methods and tools could provide inputs and data useful for didactic practical activities, such as visual representations of affective scenes, role-playing exercises, or even crowd-sourced annotation that allows students to express their personal interpretations of the characters' emotions in different chapters and situations. Activities like these can make the whole learning experience more dynamic and captivating, promoting a deeper connection between the students and the novel and, meanwhile, improving their critical thinking and empathy. The authors thank Giovanni Moretti for the help given with the fine-tuning scripts. \noindent Questa pubblicazione è stata realizzata da ricercatrice con contratto di ricerca cofinanziato dall’Unione europea - PON Ricerca e Innovazione 2014-2020 ai sensi dell’art. 24, comma 3, lett. a, della Legge 30 dicembre 2010, n. 240 e s.m.i. e del D.M. 10 agosto 2021 n. 1062.