Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
Persona-Based Synthetic Data Generation Using Multi-Stage Conditioning with Large Language Models for Emotion Recognition
In the field of emotion recognition, the development of high-performance models remains a challenge due to the scarcity of high-quality, diverse emotional datasets. Emotional expressions are inherently subjective, shaped…
Synthetic Data GenerationEmotion ClassificationEmotion RecognitionToward Efficient Speech Emotion Recognition via Spectral Learning and Attention
Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification. Several studies have adopted different methods for SER. However, existing SER methods often struggle to capture …
Speech Emotion RecognitionEmotion ClassificationData AugmentationMulti-agent Auditory Scene Analysis
Auditory scene analysis (ASA) aims to retrieve information from the acoustic environment, by carrying out three main tasks: sound source location, separation, and classification. These tasks are traditionally executed wi…
Emotion ClassificationSpeaker IdentificationMMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs
Large language models and vision-language models (which we jointly call LMs) have transformed NLP and CV, demonstrating remarkable potential across various fields. However, their capabilities in affective analysis (i.e. …
Emotion ClassificationSentiment AnalysisWhat About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative, neutral), fine-grained emotion classifi…
Emotion ClassificationEmotion RecognitionOpinion MiningEmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian
While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce EmoBench-UA, the first a…
Emotion ClassificationEmotion Classification In-Context in Spanish
Classifying customer feedback into distinct emotion categories is essential for understanding sentiment and improving customer experience. In this paper, we classify customer feedback in Spanish into three emotion catego…
ClassificationEmotion ClassificationUniversity of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection
This paper presents our approach for SemEval 2025 Task 11 Track A, focusing on multilabel emotion classification across 28 languages. We explore two main strategies: fully fine-tuning transformer models and classifier-on…
Emotion ClassificationThe Super Emotion Dataset
Despite the wide-scale usage and development of emotion classification datasets in NLP, the field lacks a standardized, large-scale resource that follows a psychologically grounded taxonomy. Existing datasets either use …
Emotion ClassificationEmotion RecognitionEmoGist: Efficient In-Context Learning for Visual Emotion Understanding
In this paper, we introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. The key intuition of our approach is that context-dependent definition of emotion …
Emotion ClassificationIn-Context LearningInterpretable Multi-Task PINN for Emotion Recognition and EDA Prediction
Understanding and predicting human emotional and physiological states using wearable sensors has important applications in stress monitoring, mental health assessment, and affective computing. This study presents a novel…
Emotion ClassificationEmotion RecognitionEmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese Metaphors
Metaphors play a pivotal role in expressing emotions, making them crucial for emotional intelligence. The advent of multimodal data and widespread communication has led to a proliferation of multimodal metaphors, amplify…
Emotional IntelligenceEmotion ClassificationMELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
Micro-expressions (MEs) are crucial psychological responses with significant potential for affective computing. However, current automatic micro-expression recognition (MER) research primarily focuses on discrete emotion…
Emotion ClassificationLarge Language ModelMicro Expression RecognitionMicro-Expression Recognition+2Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture
Multimodal sentiment analysis, a pivotal task in affective computing, seeks to understand human emotions by integrating cues from language, audio, and visual signals. While many recent approaches leverage complex attenti…
Emotion ClassificationFeature EngineeringMultimodal Sentiment AnalysisSentiment AnalysisEmo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification
Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories. Foundation large language models (LLMs) li…
Emotion ClassificationGPUKnowledge DistillationSentiment AnalysisSHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians
Accurate, real-time 3D reconstruction of human heads from monocular images and videos underlies numerous visual applications. As 3D ground truth data is hard to come by at scale, previous methods have sought to learn fro…
3D ReconstructionEmotion ClassificationIntegrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification
As the popularity and reach of social networks continue to surge, a vast reservoir of opinions and sentiments across various subjects inundates these platforms. Among these, X social network (formerly Twitter) stands as …
Emotion ClassificationSentiment AnalysisA Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
This study presents a systematic comparison of three approaches for the analysis of mental health text using large language models (LLMs): prompt engineering, retrieval augmented generation (RAG), and fine-tuning. Using …
Emotion ClassificationPrompt EngineeringRAGRetrieval+1GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations
Affective Computing (AC) is essential for advancing Artificial General Intelligence (AGI), with emotion recognition serving as a key component. However, human emotions are inherently dynamic, influenced not only by an in…
cross-modal alignmentEmotion ClassificationEmotion RecognitionEmotion Recognition in Conversation+1Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation
Emotion recognition is crucial for advancing mental health, healthcare, and technologies like brain-computer interfaces (BCIs). However, EEG-based emotion recognition models face challenges in cross-domain applications d…
Domain AdaptationEEGEmotion ClassificationEmotion Recognition+1