Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text

ArXi:2605.16613v1 Announce Type: new identification to evaluation, addressing the limitations of discrete classification in applied domains such as finance. By constructing a dataset of emotional intensity scores and fine-tuning open-weight generative language models to output continuous values from 0-100, we nstrate a expressive, generalizable framework for sentiment and emotion analysis. Our findings not only outperform classification baselines but also reveal surprising generalization capabilities and transfer effects to related constructs such as sentiment and arousal.