prikaz prve stranice dokumenta Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model
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Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model
Applied Sciences-Basel, 11 (2021), 21; https://doi.org/10.3390/app112110442
Babić, Karlo; Petrović, Milan; Beliga, Slobodan; Martinčić-Ipšić, Sanda; Matešić, Mihaela; Meštrović, Ana

Institucijski repozitorij: Repozitorij Filozofskog fakulteta Rijeka

Citirajte ovaj rad

Babić, K., Petrović, M., Beliga, S., Martinčić Ipšić, S., Matešić, M. i Meštrović, A. (2021). Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model. Applied Sciences-Basel, 11. (21). doi: 10.3390/app112110442

Babić, Karlo, et al. "Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model." Applied Sciences-Basel, vol. 11, br. 21, 2021. https://doi.org/10.3390/app112110442

Babić, Karlo, Milan Petrović, Slobodan Beliga, Sanda Martinčić Ipšić, Mihaela Matešić i Ana Meštrović. "Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model." Applied Sciences-Basel 11, br. 21 (2021). https://doi.org/10.3390/app112110442

Babić, K., et al. (2021) 'Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model', Applied Sciences-Basel, 11(21). doi: 10.3390/app112110442

Babić K, Petrović M, Beliga S, Martinčić Ipšić S, Matešić M, Meštrović A. Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model. Applied Sciences-Basel [Internet]. 2021. [pristupljeno 13.11.2024.];11(21). doi: 10.3390/app112110442

K. Babić, M. Petrović, S. Beliga, S. Martinčić Ipšić, M. Matešić i A. Meštrović, "Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model", Applied Sciences-Basel, vol. 11, br. 21, 2021. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:186:252651. [Citirano: 13.11.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:186:252651
Podaci o radu
Naslov (engleski)Characterisation of COVID-19-Related Tweets in the Croatian Language: Framework Based on the Cro-CoV-cseBERT Model
AutorKarlo Babić
AutorMilan Petrović
AutorSlobodan Beliga
AutorSanda Martinčić-Ipšić
AutorMihaela Matešić
AutorAna Meštrović
Autorova ustanovaSveučilište u Rijeci
Filozofski fakultet
Znanstveno / umjetničko
područje, polje i grana		HUMANISTIČKE ZNANOSTI
Interdisciplinarne humanističke znanosti
Znanstveno / umjetničko
područje, polje i grana		DRUŠTVENE ZNANOSTI
Informacijske i komunikacijske znanosti
Sažetak (engleski)
This study aims to provide insights into the COVID-19-related communication on Twitter in the Republic of Croatia. For that purpose, we developed an NL-based framework that enables automatic analysis of a large dataset of tweets in the Croatian language. We collected and analysed 206, 196 tweets related to COVID-19 and constructed a dataset of 10, 000 tweets which we manually annotated with a sentiment label. We trained the Cro-CoV-cseBERT language model for the representation and clustering of tweets. Additionally, we compared the performance of four machine learning algorithms on the task of sentiment classification. After identifying the best performing setup of NLP methods, we applied the proposed framework in the task of characterisation of COVID-19 tweets in Croatia. More precisely, we performed sentiment analysis and tracked the sentiment over time. Furthermore, we detected how tweets are grouped into clusters with similar themes across three pandemic waves. Additionally, we characterised the tweets by analysing the distribution of sentiment polarity (in each thematic cluster and over time) and the number of retweets (in each thematic cluster and sentiment class). These results could be useful for additional research and interpretation in the domains of sociology, psychology or other sciences, as well as for the authorities, who could use them to address crisis communication problems.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaApplied Sciences-Basel
Brojčani podacivol. 11, br. 21, str. 0
e-ISSN2076-3417
DOIhttps://doi.org/10.3390/app112110442
URN:NBNurn:nbn:hr:186:252651
Datum objave publikacije2021
ProjektŠifra: IP-CORONA-2020-04-2061
Naziv (hrvatski): Višeslojni okvir za karakterizaciju širenja informacija putem društvenih medija tijekom krize COVID-19
Kratica: InfoCoV
Voditelj: Ana Meštrović
Pravna nadležnost: Hrvatska
Financijer: HRZZ
Linija financiranja: IP-CORONA
ProjektŠifra: uniri-drustv-18-38
Naziv (hrvatski): Postupci mjerenja semantičke sličnosti tekstova
Naziv (engleski): Measuring the semantic similarity of texts
Kratica: SemText
Voditelj: Ana Meštrović
Pravna nadležnost: Hrvatska
Financijer: NadSve
Linija financiranja: Sveučilište u Rijeci
URL dokumentahttp://bib.irb.hr/1155753
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2022-12-20 10:22:09