FORECASTING BITCOIN PRICE RANGES BY COMBINING SENTIMENT ANALYSIS WITH HIGH-DIMENSIONAL INDICATORS IN AN LSTM FRAMEWORK

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.