Beyond Correlation: Refutation-Validated Aspect-Based Sentiment Analysis for Explainable Energy Market Returns

TL;DR

This paper introduces a refutation-validated framework for aspect-based sentiment analysis in energy markets, emphasizing robustness and interpretability over simple correlation, with a focus on methodological validation rather than causality.

Contribution

It presents a novel, rigorous validation pipeline for sentiment signals in financial data, addressing spurious correlations and enhancing interpretability in energy market analysis.

Findings

Few sentiment associations survive all validation tests.

Renewables show aspect and horizon specific responses.

Framework provides robust, directionally interpretable signals.

Abstract

This paper proposes a refutation-validated framework for aspect-based sentiment analysis in financial markets, addressing the limitations of correlational studies that cannot distinguish genuine associations from spurious ones. Using X data for the energy sector, we test whether aspect-level sentiment signals show robust, refutation-validated relationships with equity returns. Our pipeline combines net-ratio scoring with z-normalization, OLS with Newey West HAC errors, and refutation tests including placebo, random common cause, subset stability, and bootstrap. Across six energy tickers, only a few associations survive all checks, while renewables show aspect and horizon specific responses. While not establishing causality, the framework provides statistically robust, directionally interpretable signals, with limited sample size (six stocks, one quarter) constraining generalizability and framing this work as a methodological proof of concept.