Predviđanje cijene bitcoina na temelju analize sentimenta

Grubišić, Dora

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master's thesis

Predviđanje cijene bitcoina na temelju analize sentimenta

Split: University of Split, Faculty of economics Split, 2023. urn:nbn:hr:124:091042

Grubišić, Dora

University of Split
Faculty of economics Split

Cite this document

APA 6th Edition

Grubišić, D. (2023). Predviđanje cijene bitcoina na temelju analize sentimenta (Master's thesis). Split: University of Split, Faculty of economics Split. Retrieved from https://urn.nsk.hr/urn:nbn:hr:124:091042

MLA 8th Edition

Grubišić, Dora. "Predviđanje cijene bitcoina na temelju analize sentimenta." Master's thesis, University of Split, Faculty of economics Split, 2023. https://urn.nsk.hr/urn:nbn:hr:124:091042

Chicago 17th Edition

Grubišić, Dora. "Predviđanje cijene bitcoina na temelju analize sentimenta." Master's thesis, University of Split, Faculty of economics Split, 2023. https://urn.nsk.hr/urn:nbn:hr:124:091042

Harvard

Grubišić, D. (2023). 'Predviđanje cijene bitcoina na temelju analize sentimenta', Master's thesis, University of Split, Faculty of economics Split, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:124:091042

Vancouver

Grubišić D. Predviđanje cijene bitcoina na temelju analize sentimenta [Master's thesis]. Split: University of Split, Faculty of economics Split; 2023 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:124:091042

IEEE

D. Grubišić, "Predviđanje cijene bitcoina na temelju analize sentimenta", Master's thesis, University of Split, Faculty of economics Split, Split, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:124:091042

Cite this item: https://urn.nsk.hr/urn:nbn:hr:124:091042

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Metadata

Title	Predviđanje cijene bitcoina na temelju analize sentimenta
Title (english)	Bitcoin price prediction using sentiment analysis
Author	Dora Grubišić
Mentor	Mario Jadrić (mentor)
Granter	University of Split Faculty of economics Split Split
Defense date and country	2023, Croatia
Scientific / art field, discipline and subdiscipline	SOCIAL SCIENCES Economics Business Informatics
Abstract	Svrha ovoga rada je utvrđivanje uzročno-posljedičnih veza između sentimenta s društvenih mreža i drugih povezanih bihevioralnih čimbenika i kretanja cijene bitcoina. Također, jedan je od temeljnih ciljeva postizanje veće razine točnosti predviđanja cijene bitcoina uključivanjem sentimenta u prediktivne modele. Sukladno tome, provedena je analiza sentimenta objava s Twittera korištenjem modula VADER na uzorku od 1,64 milijuna objava. Prosječni dnevni polaritet sentimenta korišten je kao input za izgradnju više različitih modela, zajedno s prosječnim dnevnim brojem objava i brojem pretraživanja pojma bitcoin na tražilici Google i Wikipediji. S ciljem predviđanja budućih cijena, korišteni su VAR, Prophet i LSTM model, te je provedena komparacija s obzirom na postignutu točnost modela.
Abstract (english)	The main aim of this paper is to find and explore the causal relationship between social media sentiment and other related behavioral factors and bitcoin price movements. Additionally, one of the main goals was to achieve better accuracy in bitcoin price prediction with the inclusion of sentiment in predictive models. Therefore, Twitter sentiment analysis was conducted on 1,64 million tweets, with the use of VADER lexicon-based text mining library. The average daily sentiment polarity was used as an input for different models, together with the average daily tweet volume, Google search and Wikipedia search volumes. VAR, Prophet and LSTM models were used with the aim of predicting future bitcoin prices, and their performance was compared based on achieved accuracy.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:124:091042
Study programme	Title: Business Studies Study programme type: university Study level: graduate Academic / professional title: sveučilišni magistar ekonomije (sveučilišni magistar ekonomije)
Type of resource	Text
File origin	Born digital
Access conditions	Access restricted to students and staff of home institution
Terms of use
Repository	REFST
Created on	2023-10-17 11:18:18