Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora

Abstract

Sentiment analysis in low-resource languages presents unique challenges that Large Language Models may help address. This study explores the efficacy of GPT-4 for sentiment analysis on Faroese news texts, an uncharted task for this language. On the basis of guidelines presented, the sentiment analysis was performed with a multi-class approach at the sentence and document level with 225 sentences analysed in 170 articles. When comparing GPT-4 to human annotators, we observe that GPT-4 performs remarkably well. We explored two prompt configurations and observed a benefit from having clear instructions for the sentiment analysis task, but no benefit from translating the articles to English before the sentiment analysis task. Our results indicate that GPT-4 can be considered as a valuable tool for generating Faroese test data. Furthermore, our investigation reveals the intricacy of news sentiment. This motivates a more nuanced approach going forward, and we suggest a multi-label approach for future research in this domain. We further explored the efficacy of GPT-4 in topic classification on news texts and observed more negative sentiments expressed in international than national news. Overall, this work demonstrates GPT-4’s proficiency on a novel task and its utility for augmenting resources in low-data languages.