Damásio, Bruno Miguel PintoBelova, Ekaterina2025-11-052025-10-27http://hdl.handle.net/10362/190139Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Marketing Research and CRMThis thesis explores the potential of financial sentiment extracted from corporate disclosures as an indicator of industry-level economic performance. Annual filings (10-K form) submitted in 2003-2021, as mandated by the U.S. Securities and Exchange Commission (SEC), were downloaded from the EDGAR database and sentiment analysis was run on them using FinBERT. These sentiment scores were subsequently aggregated at the industry level and aligned with macroeconomic and sector-specific indicators from BLS, FRED and BEA databases. The resulting panel dataset was utilized for conducting fixed-effects regression analysis across multiple time lags to test predictive power of sentiment scores. The findings indicate that sentiment scores are associated with BLS-derived sector performance indicators, but no significant positive association was found with BEA-sourced industry performance data. The thesis provides empirical evidence supporting the value of text mining in broader macroeconomic analysis and early-warning frameworks.engText miningsentiment analysisFinBERT10-K filingsreal sectoral outputSDG 8 - Decent work and economic growthSDG 16 - Peace, justice and strong institutionsmaster thesis204070813