Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm

Abstract

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins.