Aug 2026· International Journal of Engineering Research and Science & Technology· 0 citations
TL;DR
A way to estimate the range of Bitcoin prices for the following day by combining Long Short-Term Memory (LSTM) networks with natural language processing (NLP) approaches, showing how sentiment analysis and deep learning architectures can be combined to increase forecasting resilience and interpretability in erratic cryptocurrency markets.
Abstract
In this paper, I suggest a way to estimate the range of Bitcoin prices for the following day by combining Long Short-Term Memory (LSTM) networks with natural language processing (NLP) approaches. To capture the complex sentiment of the market, the model combines sentiment elements that are collected from Twitter data using sophisticated natural language processing techniques with high-dimensional technical indicators. The LSTM model successfully learns temporal correlations and intricate patterns by integrating sequential analysis of both numerical market indicators and textual sentiment data, improving its capacity to predict changes in Bitcoin prices. The trials make use of millions of pertinent Twitter messages and six years' worth of Bitcoin market data. The method shows how sentiment analysis and deep learning architectures can be combined to increase forecasting resilience and interpretability in erratic cryptocurrency markets. Sensitivity analysis is used to maximise the impact of sentiment characteristics, emphasising the value of sentiment-driven insights in financial prediction models and providing a fresh viewpoint for more precise and dynamic forecasting of the cryptocurrency market.
A hybrid forecasting framework that integrates sentiment analysis with deep learning to predict Bitcoin’s hourly and daily closing prices and empirical results demonstrate that sentiment-enhanced hybrid models consistently outperform models based solely on technical indicators across RMSE, MAE, MAPE, and R² metrics.
Meltem Kavaklı, Kadriye Filiz Balbal· Balıkesir Üniversitesi Fen B...· 0 citations
A methodological framework based on LSTM neural networks for comparing the performance of various sets of features in predicting Bitcoin prices and proof that technical indicators have a significant impact on the predictive accuracy of this model, while social media sentiment has a minor impact under the circumstances are brought.
Sedeeq Hasan Banna, Ammar Ahmed Othman, Khaled Al-Raddah· SISTEMASI· 0 citations
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.
Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.
Financial markets are highly sensitive to investor sentiment, news events, social media discussions, and macroeconomic announcements. Traditional market prediction models primarily rely on historical price and volume data, often overlooking the behavioral aspects that significantly influence market movements. This paper presents a comprehensive sentiment analysis framework for financial market prediction using natural language processing and machine learning techniques. Financial news articles, social media posts, and corporate announcements are collected and preprocessed using tokenization, stop-word removal, stemming, and feature extraction methods such as TF-IDF and word embeddings. Sentiment scores are generated using lexicon-based and deep learning approaches, and these scores are integrated with market indicators to predict stock price direction. Experimental evaluation demonstrates that combining textual sentiment with technical indicators improves prediction performance compared with price-based models alone. The proposed approach achieves higher accuracy, precision, and recall in forecasting short-term market trends. The study highlights the importance of investor psychology in financial forecasting and demonstrates how sentiment-driven models can assist traders, portfolio managers, and financial institutions in making informed investment decisions. The proposed framework provides a scalable and data-driven solution for intelligent financial market prediction.
Alan Bundy, Karen Spärck Jones· International Journal of Com...· 1 citation
This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset.
