Synergizing High-Dimensional Technical Metrics And Crowd Sentiment For Bitcoin Volatility Classification Using Cart
DOI:
https://doi.org/10.63665/IJAICE.0203.04Keywords:
Bitcoin Price Prediction, Cryptocurrency Forecasting, Long Short-Term Memory (LSTM), Natural Language Processing (NLP), Sentiment Analysis, Twitter Data, Deep Learning, Technical Indicators, Financial Forecasting, Time-Series AnalysisAbstract
This study presents a hybrid approach for forecasting the next-day Bitcoin price range by integrating Natural Language Processing (NLP)-based sentiment analysis with a Long Short-Term Memory (LSTM) deep learning model. The proposed framework combines historical Bitcoin market data and high-dimensional technical indicators with sentiment information extracted from a large volume of Twitter posts. Advanced NLP techniques are used to analyze public opinions and capture positive, negative, and neutral market sentiment, while the LSTM network learns temporal dependencies and complex sequential patterns from both numerical and textual features.
The experimental analysis is conducted using six years of Bitcoin market data along with millions of relevant Twitter posts. By incorporating technical indicators and public sentiment simultaneously, the proposed model provides a more comprehensive understanding of market behavior than conventional approaches based only on historical price information. Sensitivity analysis is further performed to evaluate and optimize the contribution of sentiment features to the forecasting process. The results demonstrate that the inclusion of sentiment-driven information improves the accuracy, robustness, and adaptability of next-day Bitcoin price range prediction. Overall, the study highlights the potential of combining deep learning, financial indicators, and social media sentiment analysis for developing more effective and dynamic cryptocurrency forecasting systems.
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