CRYPTO SENTIMENT ANALYSIS USING MACHINE LEARNING

The rapid evolution of the cryptocurrency market, mirroring traditional currency systems but operating digitally and decentralized, marks a significant paradigm shift. Despite cryptographic safeguards, the industry remains under intense scrutiny due to its nascent nature. Cryptocurrencies, emerging as a distinct asset class driven by financial technology, present a rich landscape for research. Understanding public sentiment towards cryptocurrencies is pivotal for a comprehensive understanding of this ecosystem. This study employs a Long Short-Term Memory (LSTM) neural network to predict Bitcoin’s price in US dollars, utilizing the Keras library in Python. Renowned for its accuracy and ability to capture long-term dependencies, LSTM models offer promising avenues for price forecasting. Drawing from an extensive literature review, we validate LSTM’s effectiveness in predicting cryptocurrency prices. Our goal is to devise a robust solution applicable to various cryptocurrencies, ensuring the highest possible accuracy. This research contributes to the advancement of cryptocurrency markets, empowering informed decision-making in this dynamic financial realm. Overall, this study represents a significant advancement in the prediction of cryptocurrency sentiment, offering valuable insights for investors, traders, and policymakers alike. By harnessing the power of advanced analytics