Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled
This work addresses annotation reliability in emotion analysis for NLP researchers, but it is incremental as it builds on existing annotation studies.
The study investigated the relationship between emotion intensity, annotation confidence, and inter-annotator agreements in text annotation, finding that confidence approximates disagreements and is correlated with emotion intensity.
When humans judge the affective content of texts, they also implicitly assess the correctness of such judgment, that is, their confidence. We hypothesize that people's (in)confidence that they performed well in an annotation task leads to (dis)agreements among each other. If this is true, confidence may serve as a diagnostic tool for systematic differences in annotations. To probe our assumption, we conduct a study on a subset of the Corpus of Contemporary American English, in which we ask raters to distinguish neutral sentences from emotion-bearing ones, while scoring the confidence of their answers. Confidence turns out to approximate inter-annotator disagreements. Further, we find that confidence is correlated to emotion intensity: perceiving stronger affect in text prompts annotators to more certain classification performances. This insight is relevant for modelling studies of intensity, as it opens the question wether automatic regressors or classifiers actually predict intensity, or rather human's self-perceived confidence.