[2603.23568] Causal Reconstruction of Sentiment Signals from Sparse News Data

Computer Science > Machine Learning arXiv:2603.23568 (cs) [Submitted on 24 Mar 2026] Title:Causal Reconstruction of Sentiment Signals from Sparse News Data Authors:Stefania Stan, Marzio Lunghi, Vito Vargetto, Claudio Ricci, Rolands Repetto, Brayden Leo, Shao-Hong Gan View a PDF of the paper titled Causal Reconstruction of Sentiment Signals from Sparse News Data, by Stefania Stan and 6 other authors View PDF HTML (experimental) 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 preservat...