Forecasting Bitcoin with Hybrid Sentiment Price Ensembles: Evidence from CryptoSent Project

Forecasting Bitcoin’s price remains one of the most complex challenges in modern finance. Without traditional valuation anchors and with prices that shift on the wave of global sentiment, conventional models struggle to keep pace. The CryptoSent project confronts this challenge by building a real-time forecasting system that fuses quantitative market dynamics with natural language insights from social media and news. The system blends NeuralProphet for long-term trend capture, LSTM for short-term volatility correction, and an XGBoost model that translates sentiment and engagement patterns into predictive signals. Working together, these components generate twelve-hour forecasts that react to both technical momentum and the tone of the online conversation. Deployed on Google Cloud, the pipeline ingests minute-level data, retrains continuously, and visualizes results through an integrated dashboard. The combined ensemble significantly improves predictive accuracy over standard baselines and random walk benchmarks, offering traders a transparent and adaptive lens on the world’s most sentiment-driven asset.