Leveraging Large Language Models and Sentiment Analysis in Financial Sector (LLM-Based Bitcoin Prediction & Management System – BCM)

Abstract

The rapid growth of cryptocurrency markets has introduced significant challenges in predicting price movements due to extreme volatility and the influence of investor sentiment. Traditional forecasting methods primarily depend on historical price data and fail to incorporate contextual information from financial news and social media. To address this limitation, this paper proposes an LLM-Based Bitcoin Prediction and Management (BCM) System that integrates sentiment analysis with machine learning techniques for enhanced financial forecasting. The proposed system collects historical Bitcoin data along with real-time news and social media content. Transformer-based Large Language Models such as FinBERT and DistilBERT are utilized to extract sentiment features from textual data. These features are combined with technical indicators including price trends, trading volume, and Relative Strength Index (RSI) to form a hybrid predictive model. Experimental evaluation demonstrates that integrating sentiment-driven insights significantly improves prediction accuracy compared to traditional approaches. The BCM system achieves approximately 92% accuracy and provides risk-aware investment insights through an interactive dashboard. The results highlight the effectiveness of combining LLM-based sentiment analysis with machine learning for intelligent cryptocurrency forecasting and decision support.

Introduction

Recent advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) have improved the ability of machines to analyze financial markets using both numerical and textual data. Cryptocurrencies like Bitcoin are highly volatile and influenced not only by historical price trends and technical indicators but also by public sentiment expressed through news and social media. Traditional financial models mainly rely on statistical indicators such as Moving Averages and RSI, but they fail to capture the impact of unstructured textual information on market behavior.

To address this limitation, Large Language Models (LLMs) such as FinBERT and DistilBERT are used for contextual understanding and sentiment classification of financial text. These models, when combined with machine learning techniques, provide a more comprehensive and accurate approach to cryptocurrency prediction.

The proposed Bitcoin Management System (BCM) aims to integrate sentiment analysis with machine learning to improve forecasting accuracy and interpretability. The key objectives of the system are:

• Collecting real-time Bitcoin market data.
• Analyzing financial news using LLM-based sentiment analysis.
• Combining sentiment scores with technical indicators such as RSI.
• Developing prediction models using Linear Regression and XGBoost.
• Providing risk-aware insights through an interactive dashboard.

The literature survey shows the evolution of financial forecasting methods. Early models such as ARIMA relied only on historical price data, while later approaches used deep learning models like LSTM to capture time-series patterns. Sentiment analysis methods such as Bag-of-Words and TF-IDF were introduced but lacked contextual understanding. Transformer-based models like BERT and FinBERT significantly improved financial sentiment analysis by accurately classifying text into positive, negative, and neutral sentiments.

Existing cryptocurrency prediction systems commonly use transformer models, social media sentiment analysis, and deep learning techniques such as LSTM and XGBoost. Although these systems improve prediction accuracy, they require high computational resources, complex architectures, and often lack interpretability, making them difficult to deploy and understand.

The proposed BCM system overcomes these limitations by using lightweight LLM models, simplified machine learning pipelines, and explainable prediction mechanisms. The system integrates:

1. Real-time market data collection.
2. Financial news sentiment analysis using FinBERT.
3. Technical indicators such as RSI.
4. Prediction models including Linear Regression and XGBoost.
5. An LLM-based explanation module that generates human-readable insights.

The BCM architecture is organized into multiple layers:

• Data Collection Layer for gathering historical prices, technical indicators, and sentiment data.
• Data Preprocessing Layer for cleaning, normalization, and scaling.
• Feature Engineering Layer for combining previous prices, sentiment index, RSI, and trading volume into predictive feature vectors.
• Prediction and Visualization components for forecasting and displaying insights through an interactive dashboard built using Streamlit.

Conclusion

This paper presents a lightweight and efficient Bitcoin Prediction and Management (BCM) system that integrates Large Language Models (LLMs) with machine learning techniques to improve forecasting accuracy and interpretability. The system combines sentiment analysis, technical indicators, and predictive modeling into a comprehensive framework that captures both market trends and investor sentiment. LLM-based sentiment analysis enabled the system to extract nuanced insights from financial news and social media posts, which, when combined with historical prices, trading volumes, and technical indicators such as RSI, produced highly informative feature vectors for prediction. The integration of models such as XGBoost and LSTM ensured robust performance under volatile market conditions, while maintaining computational efficiency. The proposed system outperformed traditional forecasting methods, reducing prediction errors and providing interpretable insights that explain the influence of sentiment on price movements. The BCM system demonstrates practical utility for both academic research and real-world financial applications. By highlighting the role of LLM-driven sentiment analysis, the system underscores the importance of understanding human-driven market behavior in predictive modeling. Future extensions could focus on multi-asset prediction, integration with automated trading systems, and real-time adaptive learning to further enhance predictive capabilities and responsiveness to market dynamics. Overall, the BCM system illustrates that combining advanced NLP techniques with traditional financial modeling significantly improves forecasting accuracy while reducing computational overhead. Overall, the proposed system shows that sentiment analysis plays a crucial role in understanding financial markets, and LLM-enhanced predictive models can significantly improve forecasting accuracy while reducing computational overhead.

References

[1] Devlin, J. et al., “BERT: Pre training of Deep Bidirectional Transformers,” 2019. [2] Araci, D., “FinBERT: Financial Sentiment Analysis,” 2019. [3] McNally, S., Roche, J., and Caton, S., “Predicting the Price of Bitcoin Using Machine Learning,” 2018. [4] Vaswani, A., Shazeer, N., Parmar, N., et al., “Attention Is All You Need,” 2017. [5] Goodfellow, I., Bengio, Y., and Courville, A., Deep Learning, MIT Press, 2016. [6] Bollen, J., Mao, H., and Zeng, X., “Twitter Mood Predicts the Stock Market,” Journal of Computational Science, 2011. [7] Arslan, S., “Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition,” Computational Economics, 2025. [8] Jung, H. S., Lee, H., and Kim, J. H., “Detecting Bitcoin Sentiment: Leveraging Language Model Applications in Sentiment Analysis for Bitcoin Price Prediction,” Neural Processing Letters, 2025. [9] Seabe, P. L., Bambe Moutsinga, C. R., and Pindza, E., “Sentiment Driven Cryptocurrency Forecasting: LSTM, GRU, Bi LSTM, and Temporal Attention Model,” Social Network Analysis and Mining, 2025. [10] Bharati Vidyapeeth, A. K., Srivastava, V., Chaubey, M. K., and Sehgal, M., “Bitcoin Price Prediction Using Sentiment Analysis and Long Short Term Memory (LSTM),” International Journal of Intelligent Systems and Applications in Engineering, 2024. [11] Dang, Y., “Bitcoin Price Prediction Using a Deep Learning based Hybrid Model with Sentiment Analysis and Attention Mechanism,” ACE Publications, 2025. [12] Bhatt, S., Ghazanfar, M., and Amirhosseini, M., “Machine Learning Based Cryptocurrency Price Prediction Using Historical Data and Social Media Sentiment,” International Conference on Machine Learning & Applications, 2023. [13] Liu, Z., “Sentiment Analysis with LLMs for Predicting Trends in Bitcoin,” Journal of Computer Science and Artificial Intelligence, 2026. [14] Ud Din, R., and Khan, S. H., “TFT ACB XML: Temporal Fusion Transformer and Attention BiLSTM with XGBoost for BTC Price Forecasting,” arXiv, 2026. [15] Mehul Gautam, “Crypto Price Prediction Using LSTM + XGBoost,” arXiv, 2025. [16] Carosia, A. E. O., “Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning,” arXiv, 2024

Copyright

Copyright © 2026 K. Ravi Chand, M. Sreshtika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.