A medium frequency business sentiment analysis tool for forecasting economic indicators in India using Natural Language Processing and econometric modeling.
📊 #Overview This research project develops a Business Sentiment Index (BSI) that leverages advanced NLP techniques and neural networks to analyze business sentiment from news sources and forecast key economic indicators for the Indian economy. The BSI demonstrates superior performance compared to traditional survey-based indices in real-time economic tracking. 🎯 Key Features
High-frequency sentiment analysis from business news sources Advanced NLP pipeline using refined RoBERTa model with economist persona prompt Real-time economic forecasting capabilities Policy-weighted evaluation system that aligns with real-world applications Open-source toolkit for replication and further research
🔬 Research Findings Performance Metrics
Economic Causality: Drives economic activity with significant Granger causality (p < 0.05) for 1-month services PMI forecasting Superior Tracking: F1-score of 0.87 vs. 0.82 for OECD BCI in real-time performance Policy Alignment: Penalizes false negatives (missed downturns) 3x more than false positives, better reflecting policy concerns Strong Correlation: Achieves r=0.77 correlation with Composite PMI
🏗️ Architecture The BSI combines multiple methodologies:
Natural Language Processing: RoBERTa-based sentiment classification Time-series Econometrics: Advanced forecasting models Behavioral Economics: Policy-weighted evaluation framework Neural Networks: Deep learning for pattern recognition
📈 Key Contributions
🥇 First LLM-personalized sentiment index for emerging markets 🔬 Hybrid methodology combining NLP, econometrics, and behavioral economics 📂 Open-source toolkit for BSI replication and extension