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When humans judge the affective content of texts, they also implicitly assessthe correctness of such judgment, that is, their confidence. We hypothesizethat people's (in)confidence that they performed well in an annotation taskleads to (dis)agreements among each other. If this is true, confidence mayserve as a diagnostic tool for systematic differences in annotations. To probeour assumption, we conduct a study on a subset of the Corpus of ContemporaryAmerican English, in which we ask raters to distinguish neutral sentences fromemotion-bearing ones, while scoring the confidence of their answers. Confidenceturns out to approximate inter-annotator disagreements. Further, we find thatconfidence is correlated to emotion intensity: perceiving stronger affect intext prompts annotators to more certain classification performances. Thisinsight is relevant for modelling studies of intensity, as it opens thequestion wether automatic regressors or classifiers actually predict intensity,or rather human's self-perceived confidence.

Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled