Emotion detection research has primarily focused on English, leaving a gap for low-resource languages. To address this, we present KReLaX, a multilingual ensemble model for multi-label emotion detection, combining three BERT-based encoders with a weighted voting layer. Within the shared task, our system performed well in multi-label classification, ranking 3rd in Tatar and achieving strong results in Hindi, Russian, Marathi, and Spanish. In emotion intensity classification, we achieved 6th place in Amharic and Hausa. While our system struggled in the zero-shot track, it achieved 7th place in Indonesian. These results highlight both the potential and the challenges of multilingual emotion detection, emphasizing the need for improved generalization in low-resource settings.

Few studies have focused on detecting emotion in parliamentary corpora, and none have done this for the Finnish parliament. In this paper, this gap is addressed by applying the polarity lexicon–based methodology of a study by Rheault et al. (2016) on speeches in the British Parliament to a Finnish corpus. The findings show an increase in positive sentiment over time. Additionally, the findings indicate that politicians’ emotional states may be impacted by the state of the economy and other major events, such as the Covid-19 pandemic and the Russian invasion of Ukraine.