Sentiment Analysis Based Direction Prediction in Bitcoin using Deep Learning Algorithms and Word Embedding Models

Abstract

Sentiment analysis is a considerable research field to analyze huge amount of information and specify user opinions on many things and is summarized as the extraction of users’ opinions from the text. Like sentiment analysis, Bitcoin which is a digital cryptocurrency also attracts the researchers considerably in the fields of economics, cryptography, and computer science. The purpose of this study is to forecast the direction of Bitcoin price by analysing user opinions in social media such as Twitter. To our knowledge, this is the very first attempt which estimates the direction of Bitcoin price fluctuations by using deep learning and word embedding models in the state-of-the-art studies. For the purpose of estimating the direction of Bitcoin, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are used as deep learning architectures and Word2Vec, GloVe, and FastText are employed as word embedding models in the experiments. In order to demonstrate the contibution of our work, experiments are carried out on English Twitter dataset. Experiment results show that the usage of FastText model as a word embedding model outperforms other models with 89.13% accuracy value to estimate the direction of Bitcoin price.