Where are Emotions in Text?

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.