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Prediction of Cryptocurrency Price based on Sentiment Analysis and Machine Learning Approach

Gontyala, Sai Prasanna (2021) Prediction of Cryptocurrency Price based on Sentiment Analysis and Machine Learning Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

Due to the significant expansion in the field of social media, sentiment analysis is playing an increasingly important role in the technical world. Sentiment analysis is motivated by the fact that social media platforms such as Twitter provide a wonderful forum for the general public to voice their opinions about a product or an event. Such viewpoints allow academics to work on data mining based on public reviews and opinions, and they provide crucial insights that aid corporations in making better decisions. Also, cryptocurrencies have grown in popularity in recent years and are now accepted in nearly all nations. The derivation of emotion score, which specifies the intensity of each tweet containing the words ”bitcoin” and ”BTC,” is discussed in this study. The sentiment analysis is performed on the twitter data using the python module vaderSentiment. Using the cross-correlation statistical techniques Spearman Correlation, Pearson Correlation, and Kendall Correlation, the relationship between the derived sentiment scores and the bitcoin time series data was determined. The LSTM model with two optimizers, Rmsprop and Adam, is used to predict the closing price of bitcoin. This would assist investors in
understanding current public views around the world, and the bitcoin price would be affected as a result.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HG Finance > Money > Currency
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 02 Dec 2021 19:35
Last Modified: 02 Dec 2021 19:35
URI: https://norma.ncirl.ie/id/eprint/5163

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