Deep Learning Sentiment Analysis of News and Social Media in Geopolitical Crises

Abstract

This research analyses the impact of public sentiment sourced from news articles and social media on the Indian stock market, a period of geopolitical tension. Advanced sentiment analysis techniques were applied, including: (1) a hybrid model combining BERT with TextBlob fallback for robust inference; (2) a fine-tuned BERT model trained on domain-specific tweets and news with VADER-based weak supervision, which achieved an accuracy of 87% with strong precision and recall across sentiment classes; and (3) FinBERT, a transformer pre-trained on financial text. These models were used to analyse sentiment from news headlines and Twitter posts related to geopolitical events. Sentiment scores were aggregated over time and correlated with stock returns from key indices (Nifty 50, Sensex) and sector-specific stocks in defence, energy, IT, and banking. The study explores how sentiment dynamics align with market movements, aiming to reveal predictive and explanatory insights during high-impact geopolitical events, especially in sensitive sectors like defence, banking, and energy.