CrossRate: A Label-Free Measure for Sentiment Analysis in Visual Emotion Recognition

Images may evoke different emotional responses depending on their content, style, and viewer interpretation. This is particularly important when evaluating works of art, architectural designs, or interior design choices. Visual emotion recognition (VER) models are commonly evaluated using supervised classification metrics such as accuracy, precision, recall, and macro-F1, together with general uncertainty indicators such as entropy, maximum softmax probability (MSP), and top-1/top-2 margin. However, these measures do not indicate whether the model’s strongest competing-emotion predictions remain within the same sentiment group or cross the positive–negative sentiment boundary. This paper proposes the top-2 cross-sentiment rate (CrossRate), a label-free measure for analyzing sentiment-level ambiguity in VER models. CrossRate measures the proportion of samples for which the top-1 and top-2 predicted emotion classes belong to opposite sentiment groups. The measure is evaluated on VER datasets using both standard classification metrics and uncertainty indicators. Experiments on EmoSet-118K show that varying the model’s parameters reduces CrossRate from (22.15±0.45)% to (7.81±0.62)% and increases accuracy from (79.13±0.16)% to (80.10±0.15)%. These changes are not fully reflected by entropy, MSP, or margin, indicating that CrossRate captures a complementary aspect of sentiment-level prediction behavior. The WikiArt case study further demonstrates that CrossRate can be applied when ground-truth emotion labels are unavailable. The proposed measure is applicable to any VER model whose predicted emotion classes can be mapped into positive and negative sentiment groups. The application of CrossRate is illustrated by its use in estimating the emotions of artworks. It offers even non-art experts the opportunity to form an opinion.