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arxiv: 2605.16613 · v1 · pith:ABY2NXOEnew · submitted 2026-05-15 · 💻 cs.CL · econ.GN· q-fin.EC· q-fin.GN

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

Francesco A. Fabozzi , Dasol Kim , William N. Goetzmann This is my paper

Pith reviewed 2026-05-20 17:42 UTC · model grok-4.3

classification 💻 cs.CL econ.GNq-fin.ECq-fin.GN
keywords emotion intensitygenerative modelscontinuous evaluationfine-tuningsentiment analysisarousal transferfinance NLPaffective computing
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The pith

Fine-tuned generative language models output continuous emotion intensity scores from 0 to 100 for text, outperforming discrete classification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shifts from classifying emotions into fixed categories to evaluating their intensity along a continuous scale. It builds a dataset of intensity scores and fine-tunes open-weight generative models to produce numerical outputs between 0 and 100. This setup beats standard classification methods while also transferring to predict related constructs such as sentiment and arousal. The approach targets domains like finance, where knowing the strength of emotional content matters for decisions. A sympathetic reader would care because it supplies a practical way to quantify degrees of emotion in text instead of only detecting presence or absence.

Core claim

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 that outperforms classification baselines and reveals transfer effects to sentiment and arousal.

What carries the argument

Fine-tuning open-weight generative language models on a constructed dataset of emotional intensity scores so they directly generate continuous numerical outputs from 0 to 100 rather than discrete class labels.

Load-bearing premise

The constructed dataset of emotional intensity scores supplies a reliable training signal that lets the fine-tuned models make accurate and generalizable continuous predictions on new text and related tasks.

What would settle it

Gather fresh human ratings of emotional intensity on a held-out set of texts and check whether the model's 0-100 outputs show high correlation or low error against those ratings.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes shifting emotion analysis in NLP from discrete classification to continuous intensity evaluation. It constructs a new dataset of emotional intensity scores on a 0-100 scale and fine-tunes open-weight generative language models to regress these continuous values directly from text. The central claims are that this generative framework outperforms standard classification baselines, exhibits strong generalization to unseen text, and produces transfer effects to related constructs such as sentiment polarity and arousal, making the approach more suitable for applied domains like finance where degree of emotional content matters.

Significance. If the empirical results and generalization claims are substantiated with rigorous validation, the work could meaningfully advance NLP methodology by replacing coarse discrete labels with continuous intensity predictions, offering greater expressiveness for downstream applications that require nuanced emotional assessment.

major comments (2)
  1. [Dataset construction section] The reliability of the newly constructed intensity dataset as a training signal is load-bearing for all performance and transfer claims. The manuscript must supply the annotation protocol, label source, inter-annotator agreement statistics, and quality-control procedures used to produce the 0-100 scores; without these details it is impossible to determine whether reported outperformance reflects modeling progress or dataset-specific artifacts.
  2. [Results and evaluation section] The abstract and results sections assert outperformance over classification baselines together with transfer effects to sentiment and arousal, yet the provided description supplies no quantitative metrics, baseline descriptions, error analysis, or statistical tests. The experimental results section should include these elements (e.g., MAE/RMSE comparisons, cross-validation details, and significance tests) to support the central generalization claims.
minor comments (2)
  1. [Methods] Clarify the exact open-weight models employed and the precise fine-tuning objective used to elicit continuous 0-100 outputs.
  2. [Abstract] The abstract could more explicitly state the size of the constructed dataset and the train/test split protocol.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive review. The comments highlight important areas where additional detail will strengthen the manuscript. We address each major comment below and commit to revisions that provide the requested information without altering the core claims or methodology.

read point-by-point responses

  1. Referee: [Dataset construction section] The reliability of the newly constructed intensity dataset as a training signal is load-bearing for all performance and transfer claims. The manuscript must supply the annotation protocol, label source, inter-annotator agreement statistics, and quality-control procedures used to produce the 0-100 scores; without these details it is impossible to determine whether reported outperformance reflects modeling progress or dataset-specific artifacts.

    Authors: We agree that these details are essential for readers to evaluate the dataset's reliability. The original manuscript includes a high-level description of dataset construction but does not report the full annotation protocol, label sourcing method, inter-annotator agreement statistics, or quality-control steps. In the revised manuscript we will expand the Dataset Construction section to include: (1) the complete annotation protocol (e.g., instructions given to annotators and scale usage guidelines), (2) label source (crowd-sourced via a platform with qualification filters), (3) inter-annotator agreement metrics (Pearson correlation and Krippendorff's alpha across multiple annotators per instance), and (4) quality-control procedures (removal of low-agreement samples and outlier detection). These additions will allow direct assessment of whether performance gains reflect modeling advances rather than dataset artifacts. revision: yes

  2. Referee: [Results and evaluation section] The abstract and results sections assert outperformance over classification baselines together with transfer effects to sentiment and arousal, yet the provided description supplies no quantitative metrics, baseline descriptions, error analysis, or statistical tests. The experimental results section should include these elements (e.g., MAE/RMSE comparisons, cross-validation details, and significance tests) to support the central generalization claims.

    Authors: We acknowledge that the current Results section presents high-level findings without the quantitative detail needed to fully substantiate the claims. In the revised manuscript we will augment the Experimental Results section with: explicit MAE and RMSE values for the generative models versus the classification baselines; detailed descriptions of the baselines (fine-tuned encoder-only classifiers trained on discretized 0-100 bins); an error analysis subsection highlighting representative failure cases; the cross-validation scheme (e.g., 5-fold stratified); and statistical significance tests (paired t-tests on per-instance errors and Wilcoxon signed-rank tests for transfer-task improvements). These additions will provide rigorous support for the reported outperformance and generalization to sentiment and arousal. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical dataset construction and fine-tuning

full rationale

The paper describes a standard empirical pipeline: constructing a dataset of emotional intensity scores and fine-tuning generative models to output continuous 0-100 values, followed by evaluation against classification baselines and transfer tests. No equations, self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations appear in the provided text. The central claim rests on external evaluation rather than internal redefinition of inputs as outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that emotion intensity is a continuous, annotatable quantity that generative models can learn to predict accurately; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Emotion intensity can be meaningfully represented and annotated as a continuous scalar value between 0 and 100.
    The entire framework is built around training models to output such scores.

pith-pipeline@v0.9.0 · 5691 in / 1218 out tokens · 54517 ms · 2026-05-20T17:42:16.999571+00:00 · methodology

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Reference graph

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