You have no items in your shopping cart.
ABSTRACT
Cryptocurrencies have gained significant popularity in recent years, attracting widespread attention from investors, traders, and researchers. The volatile nature of cryptocurrency markets poses challenges for accurately predicting their price movements. In this study, we propose an enhanced model for cryptocurrency prediction that combines machine learning algorithms and sentiment analysis to improve the accuracy of price forecasts. The proposed model utilizes historical price data and incorporates sentiment analysis of social media and news sources to capture the impact of public sentiment on cryptocurrency prices. By integrating sentiment analysis, the model aims to capture the influence of market sentiments, investor emotions, and news events on cryptocurrency price fluctuations. The machine learning component of the model employs a combination of regression and classification algorithms to analyze historical price patterns and identify relevant features that correlate with future price movements. Features such as price trends, trading volume, and sentiment scores are considered in the predictive modeling process. To train and validate the model, a comprehensive dataset comprising historical cryptocurrency prices and sentiment data is collected and preprocessed. The sentiment analysis component utilizes natural language processing techniques to extract sentiment scores from textual data.
