relation: https://khub.utp.edu.my/scholars/17278/
title: Cryptocurrency Price Prediction using Long Short-Term Memory and Twitter Sentiment Analysis
creator: Sabri, M.H.B.M.
creator: Muneer, A.
creator: Taib, S.M.
description: Machine learning has become the backbone of bitcoin portfolio optimization in today's technological era. This research applies a deep neural network (DNN) model, Long Short-Term Memory (LSTM), to historical bitcoin prices and Sentiment Analysis to tweet data gathered from Twitter. The LSTM algorithm is used to train the model and forecast the future cryptocurrency price. Sentiment analysis, on the other hand, examines sentiment on Twitter to determine the relationship between sentiment and cryptocurrency price fluctuations. Sentiment analysis categorizes Twitter sentiment as positive or negative, and the fraction of positive and negative tweets is used to forecast bitcoin price fluctuations. The predicted price fluctuation data is then added to the LSTM predicted price to predict the new price for the next time frame. Finally, both models forecast future cryptocurrency prices and patterns, particularly Bitcoin. © 2022 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Sabri, M.H.B.M. and Muneer, A. and Taib, S.M.  (2022) Cryptocurrency Price Prediction using Long Short-Term Memory and Twitter Sentiment Analysis.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147415712&doi=10.1109%2fICCUBEA54992.2022.10011090&partnerID=40&md5=1c8c0b2714c342b12ba8c9a5827e726d
relation: 10.1109/ICCUBEA54992.2022.10011090
identifier: 10.1109/ICCUBEA54992.2022.10011090