Sentiment analysis of cultural differences in online comments on popular news

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F25%3A73634736" target="_blank" >RIV/61989592:15210/25:73634736 - isvavai.cz</a>

Výsledek na webu

<a href="https://reference-global.com/article/10.2478/lf-2025-0020" target="_blank" >https://reference-global.com/article/10.2478/lf-2025-0020</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/lf-2025-0020" target="_blank" >10.2478/lf-2025-0020</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sentiment analysis of cultural differences in online comments on popular news

Popis výsledku v původním jazyce

The rapid growth of online communication through social networks has created new opportunities for understanding public opinion on socially relevant issues. This research examines how sentiment analysis (SA) can reveal cultural differences, specifically analyzing Czech and Ukrainian online comments on news topics including the war in Ukraine, political discussions, public health issues (tick-borne diseases, COVID-19), LGBTQ+ community matters, and natural disasters. By comparing three large language models (GPT-3.5-Turbo, Twitter-XLM-Ro-BERTa, and Zephyr 7B) with native speaker evaluations, we assess whether AI-based sentiment analysis can accurately capture culturally-specific emotional expressions in medium-resource languages. Our dataset comprises 6,085 comments (2,999 Czech from X/Twitter, 3,086 Ukrainian from Telegram) collected during 2023, focusing on socially relevant news coverage. We employed a hybrid methodology combining machine learning analysis with expert validation by native speakers. The study addresses a critical gap in cross-cultural sentiment analysis research, as no previous studies have compared Czech and Ukrainian linguistic patterns in this context. Results demonstrate significant performance differences among models depending on language: GPT-3.5-Turbo achieved highest accuracy for Czech (p&lt;0.001), while all models performed comparably for Ukrainian. Both populations showed predominantly negative sentiment (Czech: 69.93%, Ukrainian: 68.93% via GPT-3.5), reflecting shared emotional responses to crisis events.

Název v anglickém jazyce

Sentiment analysis of cultural differences in online comments on popular news

Popis výsledku anglicky

The rapid growth of online communication through social networks has created new opportunities for understanding public opinion on socially relevant issues. This research examines how sentiment analysis (SA) can reveal cultural differences, specifically analyzing Czech and Ukrainian online comments on news topics including the war in Ukraine, political discussions, public health issues (tick-borne diseases, COVID-19), LGBTQ+ community matters, and natural disasters. By comparing three large language models (GPT-3.5-Turbo, Twitter-XLM-Ro-BERTa, and Zephyr 7B) with native speaker evaluations, we assess whether AI-based sentiment analysis can accurately capture culturally-specific emotional expressions in medium-resource languages. Our dataset comprises 6,085 comments (2,999 Czech from X/Twitter, 3,086 Ukrainian from Telegram) collected during 2023, focusing on socially relevant news coverage. We employed a hybrid methodology combining machine learning analysis with expert validation by native speakers. The study addresses a critical gap in cross-cultural sentiment analysis research, as no previous studies have compared Czech and Ukrainian linguistic patterns in this context. Results demonstrate significant performance differences among models depending on language: GPT-3.5-Turbo achieved highest accuracy for Czech (p&lt;0.001), while all models performed comparably for Ukrainian. Both populations showed predominantly negative sentiment (Czech: 69.93%, Ukrainian: 68.93% via GPT-3.5), reflecting shared emotional responses to crisis events.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

60203 - Linguistics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Linguistic Frontiers

ISSN

—

e-ISSN

2544-6339

Svazek periodika

2025

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

13

Strana od-do

1-13

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105026922593