Evaluating synthetic with real: a systematic comparison of seven LLMs for Turkish emotion classification

Towards Reliable Evaluation of Emotional Text Generation in LLMs: Human vs. Automatic Metrics

This paper introduces a robust and extensible benchmark for systematically assessing automatic metrics in emotion generation tasks, and identifies the emotion recognition score (ERS) metric, computed with DeepSeek v3.2 and GPT-5-nano, as the most reliable automatic evaluator.

A Multi-layer Linguistic Profiling Framework for Emotion Recognition in Text: A Data-Driven Natural Language Processing (NLP) Study Using the Kaggle Emotions Dataset

Background Emotion recognition in text underpins a wide range of human-centered artificial intelligence (AI) applications, yet existing frameworks either prioritize accuracy at the expense of interpretability or analyze emotional language through a single lens. This study developed and evaluated the Multi-layer Emotion...

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.

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

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...

Emotion Detection using Speech and Text – Based Sentiment Analysis: A Hybrid Multimodal Application

Detecting human emotions accurately remains a challenging problem in affective computing, particularly when systems rely on a single system. This paper presents a hybrid approach that combines speech-based acoustic features with text-based sentiment analysis to improve emotion detection. On the speech side, we trained...