🤖 AI Summary
Traditional sentiment analysis relies on discrete classification, which struggles to capture the nuanced gradations of emotional intensity required in domains such as finance. This work proposes a novel paradigm that reframes sentiment analysis as a continuous regression task by constructing a dataset annotated with fine-grained emotion intensity scores and fine-tuning open-source generative language models to predict values on a 0–100 scale. The proposed approach significantly outperforms conventional classification baselines and demonstrates strong cross-construct transferability on related affective tasks, including sentiment polarity and arousal. By enabling more precise and expressive modeling of emotional intensity, this method offers enhanced practical utility for real-world applications demanding granular affective understanding.
📝 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.