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

2026-03-22

AI Summary

This study addresses the limitations of traditional financial sentiment analysis, which often relies on spurious correlations and struggles to distinguish genuine from spurious associations. To overcome this, the authors propose a causally robust aspect-level sentiment analysis framework, validated through causal refutation tests. The approach integrates net ratio scoring, z-standardization, OLS regression with Newey-West heteroskedasticity- and autocorrelation-consistent (HAC) standard errors, and multiple robustness checks—including placebo tests, random confounder insertion, subset stability analysis, and bootstrapping—to systematically evaluate the impact of energy market sentiment signals on stock returns. Empirical results reveal that only a minority of sentiment–return relationships pass all robustness criteria, with renewable energy sectors exhibiting significant aspect- and time-window-specific effects, thereby demonstrating the framework’s effectiveness in enhancing both interpretability and causal reliability in financial sentiment analysis.

Technology Category

• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• aspect-based sentiment analysis
• spurious correlation
• refutation testing
• energy market returns
• explainable AI

Innovation

• refutation-validated
• aspect-based sentiment analysis
• Newey-West HAC errors
• causal inference
• explainable finance