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

Abstract:We introduce a novel approach to emotion modeling that shifts the focus from
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 demonstrate a more 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. This work contributes to the
interdisciplinary recontextualization of NLP by introducing emotion intensity
evaluation as an alternative to classification, arguing that this shift better aligns
with the needs of domains--such as finance--where the degree of emotional content is
central to interpretation and decision-making.

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