Applying News Sentiment Analysis to Bitcoin Price Prediction

Abstract

Bitcoin price forecasting remains difficult because cryptocurrency markets exhibit volatility, nonlinear temporal dependence, and rapid responses to external information. This study examines whether news-derived sentiment improves a Gated Recurrent Unit (GRU) model beyond historical market variables alone. Bitcoin open, high, low, close, and volume data from Binance and Bitcoin-related news from btcnews.com covered January 2020–December 2025. After relevance filtering, 12,332 articles were processed using entity-focused Aspect-Based Sentiment Analysis and FinVADER to generate mean polarity, polarity dispersion, and article-volume features. Two identical two-layer GRU models used the preceding 30-day sequence to predict the next day’s closing price: a baseline with five market variables and an enhanced model with three sentiment features. News was aggregated by publication date without an explicit market-close cut-off; therefore, look-ahead effects cannot be excluded. Data were split chronologically into 70% training, 15% validation, and 15% testing, with scaling fitted only on the training set. In the reported single run, the enhanced model achieved RMSE USD 2,052.65, MAE USD 1,525.67, MAPE 1.51%, and R² 0.9728, compared with USD 2,244.68, USD 1,743.90, 1.70%, and 0.9675 for the baseline. Across five runs, mean RMSE improved from USD 2,402.34 to USD 2,238.46, although variability was greater for the enhanced model. A one-day-ahead Diebold–Mariano test on the selected-run test-error series indicated a significant difference (DM = 3.3605; p = 0.0008), conditional on the reported error construction. Overall, news sentiment provided complementary predictive information, while temporal alignment and initialization sensitivity require further validation.