  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.
