ELIta: A New Italian Language Resource for Emotion Analysis
Authors: Eliana Di Palma (Sapienza Università di Roma, Università Roma Tre, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Emotions and language are strongly associated. In recent years, many resources have been created to investigate this association and automatically detect emotions from texts. Presenting ELIta (Emotion Lexicon for Italian), this study provides a new language resource for the analysis and detection of emotions in Italian texts. It describes the process of lexicon creation, including lexicon selection and annotation methodologies, and compares the collected data with existing resources. By offering a non-aggregated lexicon, ELIta fills a crucial gap and is applicable to various research and practical applications. Furthermore, the work utilises the lexicon by analysing the relationships between emotions and gender.

Introduction
Emotions and language are deeply interrelated human characteristics. Language serves as a tool to communicate our feelings, while affective studies have shown that emotion permeates all aspects of language AUTHOR, such as morphology AUTHOR, phonology AUTHOR, and semantics AUTHOR. This intricate relationship has recently attracted significant attention in fields such as computational linguistics, natural language processing (NLP), and affective computing. Research focusing on the identification of emotions from texts has produced various language resources, particularly emotion lexicons developed using diverse annotation methodologies, ranging from manual AUTHOR to automatic AUTHOR, and from expert judgment AUTHOR to crowdsourcing AUTHOR. Most studies follow the dimensional approach to emotions AUTHOR. According to this perspective, the PAD (Pleasure, Arousal, Dominance) AUTHOR or VAD (Valence, Arousal, Dominance) AUTHOR model posits that the fundamental dimensions of valence (the intrinsic attractiveness (positive valence) or aversion (negative valence) of an event, object or situation), arousal (the level of physiological activation, ranging from sleep to excitement) and dominance (the degree of control a person feels over a situation) explain the majority of the emotional meaning of words. This approach has been highly productive for research on emotional language and the creation of language resources, exemplified by the ANEW (Affective Norms for English Words) AUTHOR, NRC VAD AUTHOR, and the EmoBank corpus AUTHOR. Alternatively, some researchers argue for the existence of a limited number of discrete primary emotion categories that have evolved to serve various adaptive functions through specific neural signatures, facial expressions, cognitive evaluations, and behavioral action tendencies AUTHOR. These basic emotions typically include joy, sadness, disgust, anger, fear, surprise, whereas \citeauthor{plutchik1980general} also considers trust and anticipation. Despite objections to the basic emotions model AUTHOR, it has inspired the creation of resources such as the NRC Lexicon (EmoLex) AUTHOR (translated into over 100 languages, it's the most widely used lexicon in emotion detection), and the datasets Feel It AUTHOR and Multiemotion It AUTHOR. More recently, the field of computational linguistics and NLP has recognized the need for resources specifically created for languages other than English. Critics argue against relying solely on translations, advocating for lexicons created from texts in the target language and manually annotated AUTHOR. This approach has led to the development of lexicons like the Portuguese emotional lexicon AUTHOR, which embodies the principle of "each language for itself." For the Italian language several language resources with emotional annotations have been produced over the years. The initial ItEM lexicon AUTHOR began by collecting seed words through an association task linking words to labels (\citeauthor{plutchik1980general}'s basic emotions), then employed cosine similarity to expand the lexicon, assuming that neighboring words in semantic space share similar emotional connotations. The results, validated through crowdsourcing, showed low reliability for the emotions trust, anticipation (translated as ‘attese') and surprise. The more recent Depeche Mood ++ AUTHOR was automatically created from judgements given by readers of articles on the ‘Corriere della Sera’ newspaper website and uses a unique scale of emotions not directly comparable to others, such as ANNOYED, AFRAID, SAD, AMUSED, and HAPPY. AUTHOR. In the case of Affective Norms AUTHOR, the tendency to create resources by adapting the English model with annotations in L1 languages other than English has resulted in Affective Norms for several languages, including Spanish AUTHOR and Dutch AUTHOR. For Italian, there has been a specific adaptation of the ANEW collected by AUTHOR. Despite the existing resources in the literature, a notable gap persists. There is a lack of manually annotated Italian language resources that combine both discrete emotion annotations and dimensional evaluations. Furthermore, no available resource provides a non-aggregated version of the data. This paper presents ELIta (Emotion Lexicon for Italian), an innovative resource designed for the analysis of emotions in the Italian language and emotion detection from text. ELIta aims to bridge this gap by providing a lexicon annotated using both categorical and dimensional approaches, and by offering a non-aggregated version of the data. This aligns with the perspectivist viewpoint, which values disagreement as valuable information AUTHOR. The development process of ELIta, including lexicon selection, annotation methodologies, and a comparative analysis with existing Italian sentiment lexicons, is thoroughly described. Finally, analyses of the relationships between emotions and between dimensions and gender are presented.

Conclusion
This research introduces a new lexicon for Italian that collects word-emotion associations. Notably, it is the first lexicon, to the authors' knowledge, to be annotated using both categorical and dimensional approaches. Furthermore, it offers an innovative non-aggregated version of the data, reflecting a ‘perspectivist’ approach that values disagreement as valuable information, such as women showing a greater tendency towards negative valence and higher arousal ratings than man. Analyses using correlations between basic emotions and dimensions, along with comparisons to existing resources such as ANEW, underscore the lexicon's potential to deepen our understanding of the interplay between emotions and language. While ELIta represents a significant step forward in capturing the complexity of emotion-language interactions in Italian, continued development will be essential to addressing its current limitations and maximizing its utility as a comprehensive tool for emotional analysis. \appendix