Where are Emotions in Text? A Human-based and Computational Investigation of Emotion Recognition and Generation (2024)

Abstract: Natural language processing (NLP) boasts a vibrant tradition of emotion studies, unified by the aim of developing systems that generate and recognize emotions in language. The computational approximation of these two capabilities, however, still faces fundamental challenges, as there is no consensus on how emotions should be processed, particularly in text: application-driven works often lose sight of foundational theories that describe how humans communicate what they feel, resulting in conflicting premises about the type of data best suited for modeling and whether this modeling should focus on textual meaning or style. My thesis fills in these theoretical gaps that hinder the creation of emotion-aware systems, demonstrating that a trans-disciplinary approach to emotions, which accounts for their extra-linguistic characteristics, has the potential to improve their computational processing. I investigate the human ability to detect emotions in written productions, and explore the linguistic dimensions that contribute to the emergence of emotions through text. In doing so, I clarify the possibilities and limits of automatic emotion classifiers and generators, also providing insights into where systems should model affective information.

## zooming in: emotions inside linguistic layers if asked what dimension of language allows to infer emotion, early computational research @xcite would reply: semantics. many words have a prototypical affective connotation, and that is why, e.g., "win" evokes joy, while "darkness" fear. the correspondence between emotions and meaning is an idea that keeps taking hold of the field, but it is just a commonsense assumption. for that matter, also the alternative equivalence between emotions and style remains unverified. the goal of the second strand of research in my thesis is to learn if either of these potentially competing views, emotions as linguistic styles (chapter 5) or emotions as meanings (chapter 6), is valid. i abandon the discussion of the relationship between people and emotions, narrowing the spotlight on data (cf. figure 3 ) and leveraging computational systems (text generators and classifiers in chapter 5, only classifiers in chapter 6) for tasks that would require too intensive human labour. the results presented in both chapters have an applicative value for nlp. by identifying the linguistic level at which emotions arise, i determine whether computational tasks can model emotions as parts of style or word meanings. my contribution, however, is mainly theoretical: to explore the information needed to imbue and capture emotions in texts is to explore how emotions, which are primarily cognitive phenomena, turn into linguistic ones. ## the four problems, revised my thesis opens a channel of communication between nlp studies and other disciplines based on emotions. i find that (1) emotions can be modeled in the guise of "untold things" which are extracted from text, though imperfectly, because (2) their interpretation rests on factors in and out of language, (3) with linguistic style playing only a marginal role, and (4) meaning (which can be fuzzy and subject to many potential reads) being the primary source of their emergence. hence, where are emotions (generated and recognized) in text? they are in a text semantics: in the semantics of events. by reviewing the discussion in retrospect, it appears clear that this finding gradually peeps out from each of the core chapters. chapter 3 discusses emotions in factual descriptions, all with the same highly-structured, factual style (hardly could it contribute to their linguistic realization). chapter 4 proves that differences in the emotions recognized in text are backed up by differences in interpretations of the described events (i.e., in people's background knowledge about the affective meaning of events). chapter 5 corroborates that generating text with a given emotion is unfeasible if one only cares about the visible layer of language (looking at a deeper level is necessary). lastly, chapter 6 uses frame semantics to study meaning troiano irrespective of the lexical units that instantiate it (i.e., to peel off style), declaring frames the basic units of emotion, which capture the link with events and event judgments that, according to psychology, lies at the very heart of this complex phenomenon. emotions in today's nlp. one year after my thesis defense, large language models have unprecedented linguistic skills. as chatgpt recognizes emotions with enormous accuracy @xcite and generates them on demand @xcite , it comes natural to ask if the dialogue between technologies and theories that i advised is still relevant. i believe it is. in fact, my cross-disciplinary approach now has even grater potential to push research forward: nlp tasks can still inform theories, thanks to systems that have learned their emotion abilities from gigantic amounts of verbal data, and that give the chance to search the "rules" of emotions, by observing how they use them in text. vice versa, theories (e.g., from psychology) can keep enhancing computational models, especially since these models interact with the general population today. emotions serve to convey meanings and to carry out natural sounding conversations. thus, it is still important to tackle the issue of what it means for a system to be emotionally proficient, considering that such a proficiency deals with a fundamental subjectivity (e.g., of event evaluations) that no speaker, human or artificial, can escape.