EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis

By Sven Buechel, Udo Hahn. Conference of the European Chapter of the Association for Computational Linguistics, 2017

311 citations recorded by the source corpus.

We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer’s and reader’s emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader’s perspective in terms of IAA and rating intensity, and achieve close-to-human performance when mapping between dimensional and categorical formats.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/5607fcc4a83de6ef489d4b0e4c94689001f07d69

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Citations in the corpus

Transformer models for text-based emotion detection: a review of BERT-based approaches (2021, unknown): “The proposed Park et al. model was evaluated using the EmoBank data and obtained a micro F1 score of 0.688 and 0.695 when fine-tuned on the ISEAR and SemEval datasets, respectively.”
Modeling Empathy and Distress in Reaction to News Stories (2018, background): “Only very recently, researchers started exploring more sophisticated models of human emotion on a larger scale (Wang et al., 2016; Abdul-Mageed and Ungar, 2017; Mohammad and Bravo-Marquez, 2017a; Buechel and Hahn, 2017, 2018a,b).”
GoEmotions: A Dataset of Fine-Grained Emotions (2020, background): “Ekman (1992a) and/or along affective dimensions (valence and arousal) that underpin the circumplex model of affect (Russell, 2003; Buechel and Hahn, 2017).”
Learning and Evaluating Emotion Lexicons for 91 Languages (2020, background): “…an increasing number of researchers focus on more expressive encodings for affective states inspired by distinct lines of work in psychology (Yu et al., 2016; Buechel and Hahn, 2017; Sedoc et al., 2017; Abdul-Mageed and Ungar, 2017; Bostan and Klinger, 2018; Mohammad, 2018; Troiano et al., 2019).”
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “While several datasets (Strapparava and Mihalcea, 2007; Buechel and Hahn, 2017) include a small set of labels representing the six Ekman emotions (Ek-man, 1992) — anger , disgust , fear , joy , sadness , and surprise ), or bipolar dimensions of affect — arousal and valence (Russell, 2003), others…”
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (2018, background): “…variety of mutually incompatible encodings schemes for emotion and, consequently, annotation formats for emotion metadata in corpora have emerged that hinder the interoperability of these resources and their subsequent reuse, e.g., on the basis of alignments or mergers (Buechel and Hahn, 2017).”