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

TL;DR

This paper presents a new generative approach for evaluating emotion intensity in text, moving beyond simple sentiment classification to produce continuous scores, with applications in finance and other domains.

Contribution

It introduces a dataset and fine-tunes generative language models for emotion intensity evaluation, outperforming classification methods and enabling better domain-specific interpretation.

Findings

Outperforms classification baselines in emotion intensity evaluation

Shows strong generalization and transfer to sentiment and arousal tasks

Provides a more expressive framework for emotion analysis in NLP

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.