Bitcoin Price Prediction Using Sentimental Analysis - A Comparative Study of Neural Network Model for Price Prediction

Abstract

The goal of this project is to develop a system that can predict the price of a cryptocurrency (Bitcoin) based on the sentiment of the input provided. This input will be supplied to the model using the Cryptopanic API, which will extract the latest news related to Bitcoin. These technological advancements can help us make accurate predictions thereby facilitating investments. We have tried to accomplish this by using a series of deep learning techniques and methodologies. Our decision to build this model using LSTM was based on the comparison of results between other algorithms like CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit) and RNN (Recurrent Neural Network). Unlike technical analysis methods which are used for normal stock market prediction we have built a model which will be trained to classify news headlines based on the sentiment detected and give a predicted price. We believe that the use of LSTM to give accurate price prediction would be extremely useful for novice as well as professional Bitcoin traders. Also, it has been proven that public sentiments have been very influential in determining the price of Bitcoin and thus taking that into consideration would improve our understanding and prediction.