EconECPE (Sentic Labcast by Simone Adobati)

Sentiment analysis often reduces investor affect to scalar polarity, overlooking the distinct market implications of emotions such as fear of inflation versus geopolitical shocks. We introduce EconECPE, the first span-level, multi-pair, typed emotion-cause extraction dataset for financial social media, built from the complete 2018–2026 r/economics archive (134,315 posts, 5.16M comments). Each annotation captures emotion, verbatim emotion and cause spans, a 16-class economic cause taxonomy, target asset, and intensity. Silver labels for ~20,000 comments are generated through multi-LLM consensus and validated against gold annotations (emotion κ = 0.80). A distilled 9B model achieves 60.6% emotion-span F1 at ~$5 versus ~$1,700 per million comments. Cause-conditioned emotions do not improve forward-return prediction beyond scalar sentiment after multiple-testing correction, while emotion aggregates track VIX across two communities, generalize to individual stocks, and reveal an out-of-sample replicable equity-reversal signal following euphoria peaks. Data, specifications, and models are released. https://sentic.net/econecpe.pdf