Erik Cambria

Erik Cambria is a Professor at Nanyang Technological University, where he also holds the appointment of Provost Chair in Computer Science and Engineering, and Founder of several AI companies, such as SenticNet (https://business.sentic.net), offering B2B sentiment analysis services, and finaXai (https://finax.ai), providing fully explainable financial insights. Prior to moving to Singapore, he worked at Microsoft Research Asia (Beijing) and HP Labs India (Bangalore), after earning his PhD through a joint program between the University of Stirling (UK) and MIT Media Lab (USA). Today, his research focuses on neurosymbolic AI for interpretable, trustworthy, and explainable affective computing in domains like social media monitoring, financial forecasting, and AI for social good. He is ranked in Clarivate’s Highly Cited Researchers List of World’s Top 1% Scientists, is recipient of many awards, e.g., IEEE Outstanding Early Career, was listed among the AI’s 10 to Watch, and was featured in Forbes as one of the 5 People Building Our AI Future. He is an IEEE Fellow, Associate Editor of various top-tier AI journals, e.g., Information Fusion and IEEE Transactions on Affective Computing, and is involved in several international conferences as keynote speaker, program chair and committee member. 

Multilingual Emotion Recognition: Discovering the Variations of Lexical Semantics between Languages

The task of multilingual emotion recognition holds significant importance in cross-cultural communication and data mining. While prior research has concentrated on enhancing classification accuracy using state-of-the-art techniques, it has often overlooked a crucial linguistic aspect—the semantic disparities across different languages. This study aims to address this gap by introducing a novel method to identify lexical semantic variations in diverse languages. The detected semantic variation features are subsequently injected into a multilingual emotion recognition model to enhance its performance within a target language. Notably, existing multilingual pre-trained language models are likely biased toward English word meanings, leading to inaccurate emotion predictions in other languages due to the misinterpretation of semantics. Our proposed semantic variation injection method tackles this limitation, resulting in improved accuracy. These findings contribute to the ongoing development of robust and culturally sensitive emotion recognition systems, offering valuable insights for both the linguistics and computational linguistics communities engaged in multilingual research.