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

Cite this document

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

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

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

Grubišić, D. (2023). 'Predviđanje cijene bitcoina na temelju analize sentimenta', Master's thesis, University of Split, Faculty of economics Split, accessed 11 April 2025, https://urn.nsk.hr/urn:nbn:hr:124:091042

Grubišić D. Predviđanje cijene bitcoina na temelju analize sentimenta [Master's thesis]. Split: University of Split, Faculty of economics Split; 2023 [cited 2025 April 11] Available at: https://urn.nsk.hr/urn:nbn:hr:124:091042

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

Metadata
TitlePredviđanje cijene bitcoina na temelju analize sentimenta
Title (english)Bitcoin price prediction using sentiment analysis
AuthorDora Grubišić
MentorMario Jadrić (mentor)
GranterUniversity of Split
Faculty of economics Split
Split
Defense date and country2023, 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... More 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. Less
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... More 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. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:124:091042
Study programmeTitle: Business Studies
Study programme type: university
Study level: graduate
Academic / professional title: sveučilišni magistar ekonomije (sveučilišni magistar ekonomije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2023-10-17 11:18:18
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