Appraisal Theories for Emotion Classification in Text

Abstract

Automatic emotion categorization has been predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory, for instance following the fundamental emotion classes proposed by Paul Ekman (fear, joy, anger, disgust, sadness, surprise) or Robert Plutchik (adding trust, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable situation. This emotion reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words "I am afraid."). Automatic classification approaches therefore need to learn properties of events as latent variables (for instance that the uncertainty and the mental or physical effort associated with the encounter of a snake leads to fear). With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and show their potential for emotion classification when being encoded in classification models. Our results show that high quality appraisal dimension assignments in event descriptions lead to an improvement in the classification of discrete emotion categories. We make our corpus of appraisal-annotated emotion-associated event descriptions publicly available.

Towards Label-Agnostic Emotion Embeddings

“…At the heart of computational emotion representation lies a set of emotion variables ("classes", "constructs") used to capture different facets of affective meaning. Researchers may choose from a multitude of approaches designed in the long and controversial history of the psychology of emotion (Scherer, 2000;Hofmann et al, 2020). A popular choice are so-called basic emotions (Alm et al, 2005;Aman and Szpakowicz, 2007;Strapparava and Mihalcea, 2007), such as the six categories identified by Ekman (1992): Joy, Anger, Sadness, Fear, Disgust, and Surprise (BE6, for short).…”

“…Other theories influential for NLP include Plutchik's (2001) Wheel of Emotion (Mohammad and Turney, 2013;Abdul-Mageed and Ungar, 2017;Tafreshi and Diab, 2018;Bostan et al, 2020) and appraisal dimensions (Balahur et al, 2012;Troiano et al, 2019;Hofmann et al, 2020). Yet frequently, studies do not follow any of these established approaches but rather design a customized set of variables in an ad-hoc fashion, often driven by the availability of user-labeled data in social media, or the specifics of an application or domain which requires attention to particular emotional nuances (Bollen et al, 2011;Desmet and Hoste, 2013;Staiano and Guerini, 2014;Qadir and Riloff, 2014;Li et al, 2016;Demszky et al, 2020).…”

Performance comparison of TF-IDF and Word2Vec models for emotion text classification

“…The classification of emotion types from texts is of concern in the field of human-computer interaction (HCI), information retrieval (IR), and has been implement in many domains [4], [5]. Emotion classification in the text have received much attention to recognize human emotions in the text [6]- [8].…”

“…where: uj: output line j to the hidden layer W′ T : transpose of the weight from the hidden layer to the output layer The number of neurons used in the output layer is the same as the number of neurons in the input layer that represents the target word. The output layer uses the Softmax activation function, where the Softmax activation function is shown in (6).…”

Emotion Recognition under Consideration of the Emotion Component Process Model

“…While end-to-end learning and fine-tuning of pretrained models for classification have shown great performance improvements in contrast to purely feature-based methods, such approaches typically neglect the existing knowledge about emotions in psychology (which might help in classification and to better understand how emotions are communicated). There are only very few approaches that aim at combining psychological theories (beyond basic emotion categories) with emotion classification models: We are only aware of the work by Hofmann et al (2020), who incorporate the cognitive appraisal of events, and Buechel et al (2020), who jointly learn affect (valence, arousal) and emotion classes; next to knowledge-base-oriented modelling of events by Balahur et al (2012) and Cambria et al (2014).…”