Twitter Mining based Forecasting of Cryptocurrency using Sentimental Analysis of Tweets

Abstract

The decentralization of cryptocurrency has decreased the level of central control, which has impacted international trade and ties. There is also an urgent need for a credible way of projecting the price of cryptocurrencies, which is currently unavailable. A novel method to predict cryptocurrency price is proposed in this paper, which makes use of deep learning techniques such as the recurrent neural network (RNN), gated recurrent unit (GRU), convolution 1D, and the long short-term memory (LSTM). This method considers a variety of factors such as market capitalization, volume, circulating supply, and maximum supply. It is more accurate at recognizing long-term relationships than the LSTM. Developed in Python, the proposed approach was tested on a range of real-world data sets. The findings demonstrate that the proposed method may be used to properly predict the price of cryptocurrencies.