UAReviews: A Multi-Task Ukrainian Dataset for Emotion and Intent Classification

Between accuracy and empathy: A comparative study of human and transformer-based multi-label emotion annotation

This study examines structural alignment between human and transformer-based multi-label emotion annotation in a low-resource language setting. A corpus of 408 Serbian narratives was manually annotated using soft-label scores mapped to the 28-category GoEmotions taxonomy. Inter-annotator agreement was moderate (micro...

Emotion Detection in Early Modern Spanish Correspondence: A Multilabel Transformer Approach

An Agentic Multi-LLM Intersection Approach to Historical Spanish Emotion Detection

TRANSFORMER-BASED EMOTION CLASSIFICATION FOR INDONESIAN POLITICAL SPEECH

This study aims to classify emotions in Indonesian political speeches using the RoBERTa Transformer model. The dataset comprises 2,952 paragraphs derived from transcripts of closed-meeting political speeches delivered between 2014 and 2024. The research methodology encompasses data collection, augmentation, automatic e...

A Transformer-Based Approach with Data Augmentation for Multilabel Emotional Context Detection

A fine-tuned Robustly Optimized Bidirectional Encoder Representations from Transformers Approach (RoBERTa) is used to offer a Transformer-based model for identifying multilabel emotional context in textual data to balance emotion categories and enhance the model's capacity for generalization.

Stacking Approaches for Multi-Label Emotion Recognition in Persian Using Large and Small Transformer Models

A hybrid model, termed SE_LLM_ST, is introduced, which leverages recent advancements in large language models (LLMs) and transformer-based architectures to effectively capture both contextual and sequential information vital for Persian emotion recognition.