2025/06/04 by Л. П. Морозов, Morozov, Lev, Aleksandr Mogilevskii +3
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Emotion and Mood Recognition #FOS: Computer and information sciences #Mental Health via Writing #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.2506.04409
openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper describes EmoRAG, a system designed to detect perceived emotions in text for SemEval-2025 Task 11, Subtask A: Multi-label Emotion Detection. We focus on predicting the perceived emotions of the speaker from a given text snippet, labeling it with emotions such as joy, sadness, fear, anger, surprise, and disgust. Our approach does not require additional model training and only uses an ensemble of models to predict emotions. EmoRAG achieves results comparable to the best performing systems, while being more efficient, scalable, and easier to implement.