Naslov
Topic modelling and sentiment analysis of COVID-19 related news on Croatian Internet portal

Autori
Buhin Pandur, Maja ; Dobša, Jasminka ; Beliga, Slobodan ; Meštrović, Ana

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceeding of Conference on Data Mining and Data Warehouses 2021 / Grobelnik, Marko ; Mladenić, Dunja - Ljubljana, 2021

Skup
Slovenian KDD Conference on Data Mining and Data Warehouses (SiKDD 2022)

Mjesto i datum
Ljubljana, Slovenija, 04.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
news media ; sentiment ; emotions ; pandemic ; lexicon approach ; Latent Dirichlet Allocation

Sažetak
The research aims to identify topics and sentiments related to the COVID-19 pandemic in Croatian online news media. For analysis, we used news related to the COVID-19 pandemic from the Croatian portal Tportal.hr published from 1st January 2020 to 19th February 2021. Topic modelling was conducted by using the LDA method, while dominant emotions and sentiments related to extracted topics were identified by National Research Council Canada (NRC) word-emotion lexicon created originally for English and translated into Croatian, among other languages. We believe that the results of this research will enable a better understanding of the crisis communication in the Croatian media related to the COVID-19 pandemic.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti, Interdisciplinarne društvene znanosti

POVEZANOST RADA