Causal Reconstruction of Sentiment Signals from Sparse News Data
arXiv:2603.23568v1 Announce Type: new Abstract: Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: given probabilistic sentiment outputs from a fixed classifier, recover a stable latent sentiment series that is robust to the structural pathologies of news data such as sparsity, redundancy, and classifier uncertainty. We present a modular three-stage pipeline that (i) aggregates article-level scores onto a regular temporal grid with uncertainty-aware and redundancy-aware weights, (ii) fills coverage gaps through strictly causal projection rules, and (iii) applies causal smoothing to reduce residual noise. Because ground-truth longitudinal sentiment labels are typically unavailable, w
arXiv:2603.23568v1 Announce Type: new Abstract: Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem. Rather than treating this as a classification challenge, we propose to frame it as a causal signal reconstruction problem: given probabilistic sentiment outputs from a fixed classifier, recover a stable latent sentiment series that is robust to the structural pathologies of news data such as sparsity, redundancy, and classifier uncertainty. We present a modular three-stage pipeline that (i) aggregates article-level scores onto a regular temporal grid with uncertainty-aware and redundancy-aware weights, (ii) fills coverage gaps through strictly causal projection rules, and (iii) applies causal smoothing to reduce residual noise. Because ground-truth longitudinal sentiment labels are typically unavailable, we introduce a label-free evaluation framework based on signal stability diagnostics, information preservation lag proxies, and counterfactual tests for causality compliance and redundancy robustness. As a secondary external check, we evaluate the consistency of reconstructed signals against stock-price data for a multi-firm dataset of AI-related news titles (November 2024 to February 2026). The key empirical finding is a three-week lead lag pattern between reconstructed sentiment and price that persists across all tested pipeline configurations and aggregation regimes, a structural regularity more informative than any single correlation coefficient. Overall, the results support the view that stable, deployable sentiment indicators require careful reconstruction, not only better classifiers.
Executive Summary
This article presents a novel approach to reconstructing sentiment signals from sparse news data, framing it as a causal signal reconstruction problem. The proposed three-stage pipeline aggregates article-level scores, fills coverage gaps, and applies causal smoothing to reduce noise. The authors develop a label-free evaluation framework and demonstrate the effectiveness of their method using a multi-firm dataset of AI-related news titles, finding a consistent three-week lead-lag pattern between reconstructed sentiment and stock prices. This approach has significant implications for financial analysis and technology monitoring, emphasizing the importance of careful reconstruction over better classifiers. The authors' methodological innovation and empirical findings make a valuable contribution to the field of sentiment analysis.
Key Points
- ▸ Reframes sentiment analysis as a causal signal reconstruction problem
- ▸ Proposes a modular three-stage pipeline for aggregating and smoothing sentiment scores
- ▸ Develops a label-free evaluation framework for assessing signal stability and causality compliance
Merits
Strength
The proposed approach innovatively addresses a significant challenge in sentiment analysis, namely the reconstruction of reliable temporal series from sparse news data.
Strength
The authors' methodological framework provides a valuable contribution to the field, enabling the development of more robust and deployable sentiment indicators.
Demerits
Limitation
The proposed method relies on probabilistic sentiment outputs from a fixed classifier, which may not generalize to other classification models or datasets.
Limitation
The evaluation framework, while innovative, may not capture all aspects of signal quality and stability, potentially leading to biased assessment.
Expert Commentary
The authors' innovative approach to sentiment analysis represents a significant methodological advancement, addressing a long-standing challenge in the field. By reframing sentiment analysis as a causal signal reconstruction problem, the authors provide a valuable contribution to the field, enabling the development of more robust and deployable sentiment indicators. While the proposed method has limitations, the authors' empirical findings provide strong support for their approach. The implications of this work are far-reaching, with significant potential benefits for financial analysis and technology monitoring, as well as policy-making in these areas.
Recommendations
- ✓ Future research should explore the generalizability of the proposed approach to other classification models and datasets.
- ✓ The authors' methodological framework should be adapted and extended to address other challenges in sentiment analysis, such as handling linguistic variability and cultural differences.
Sources
Original: arXiv - cs.LG
