Cross Lingual Emotion

Cross-lingual emotion detection aims to understand and classify emotions expressed in text and speech across multiple languages, facilitating global-scale sentiment analysis and improved cross-cultural communication. Current research focuses on developing robust models, often employing large language models, transfer learning techniques, and diffusion models, to overcome challenges posed by linguistic diversity and limited resources for many languages. These advancements are crucial for applications ranging from public opinion monitoring and mental health assessment to improving the expressivity of machine translation and speech synthesis systems. The field is actively addressing issues of data scarcity, particularly for low-resource languages, and exploring methods to account for linguistic and cultural variations in emotional expression.