The ABBE Corpus: Animate Beings Being Emotional

TL;DR

The paper introduces the ABBE corpus, a new annotated dataset capturing which animate beings experience emotions in texts, to improve emotion detection systems by including experiencer information.

Contribution

It presents a novel double-annotated corpus focusing on animate beings as emotion experiencers, with detailed annotation scheme and high inter-annotator agreement.

Findings

Corpus contains 2,010 emotion expressions attributed to 2,227 beings.

Annotations follow Plutchik's 8-category emotion model.

High inter-annotator agreement of 0.83 Cohen's Kappa.

Abstract

Emotion detection is an established NLP task of demonstrated utility for text understanding. However, basic emotion detection leaves out key information, namely, who is experiencing the emotion in question. For example, it may be the author, the narrator, or a character; or the emotion may correspond to something the audience is supposed to feel, or even be unattributable to a specific being, e.g., when emotions are being discussed per se. We provide the ABBE corpus -- Animate Beings Being Emotional -- a new double-annotated corpus of texts that captures this key information for one class of emotion experiencer, namely, animate beings in the world described by the text. Such a corpus is useful for developing systems that seek to model or understand this specific type of expressed emotion. Our corpus contains 30 chapters, comprising 134,513 words, drawn from the Corpus of English Novels, and contains 2,010 unique emotion expressions attributable to 2,227 animate beings. The emotion expressions are categorized according to Plutchik's 8-category emotion model, and the overall inter-annotator agreement for the annotations was 0.83 Cohen's Kappa, indicating excellent agreement. We describe in detail our annotation scheme and procedure, and also release the corpus for use by other researchers.