Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks
Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweight temporal convolutional networks (TCNs)…
Emotion ClassificationEmotion RecognitionMIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music
Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplo…
Representation LearningEmotion ClassificationBenchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset
Video emotion analysis is typically framed as a static classification problem, treating each clip as an independent labeled unit. However, such a formulation overlooks a key psychological fact: emotions change as a resul…
Emotion ClassificationCharacterizing the Temporal, Emotional, and Social Patterns of Adolescent Substance Use Discussions on Reddit
Adolescence is a critical developmental period marked by heightened emotional sensitivity, social stress, and vulnerability to substance use. However, traditional research methods provide limited access to adolescents' a…
Emotion ClassificationQuantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies
Emotion recognition in natural language is a foundational challenge in affective computing, with critical implications for human-computer interaction, mental health support, and conversational AI. This paper presents a r…
Emotion ClassificationEmotion RecognitionLLM-Based Synthetic Ground Truth Generation for Audio-Based Emotion Classification via In-Context Learning
Understanding human states and interaction dynamics is a core goal of human-computer interaction (HCI). As interaction paradigms become more immersive, virtual reality (VR) has emerged as a powerful platform for studying…
Emotion ClassificationBefore Fusion, Ask What to Keep: Contextual Calibration of Multimodal Signals
Multimodal systems often benefit from combining information across language, sound, and visual streams, but this benefit is not guaranteed. A modality that is useful for one input may become distracting for another, and …
Emotion ClassificationAction RecognitionData filtering methods for training language models
Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noise into training data and reduce model generalization. In this work, w…
Linguistic AcceptabilityEmotion ClassificationLabel Error DetectionText ClassificationidSCD: Identifying Training Datasets through Semantic Correlation Descriptors
Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned semantic correlation structure: incidental regularities tha…
Natural Language InferenceEmotion ClassificationText ClassificationMultimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods that rely on individual-level cues such as face, gaze, or voice analy…
Emotion ClassificationEmotion RecognitionData AugmentationUncertainty Decomposition via Cyclical SG-MCMC and Soft-label Learning for Subjective NLP
Annotator disagreement in emotion classification reflects ambiguity intrinsic to emotion concepts and is essential for predictor-quality assessment in subjective NLP. Yet no prior work integrates soft-label learning with…
Emotion ClassificationNavigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognition
Multimodal emotion recognition aims to integrate text, audio, and video sources to understand human affective states. Although multimodal large language models excel at multimodal reasoning, they typically treat emotion …
Multimodal Emotion RecognitionEmotion ClassificationMultimodal ReasoningBeyond Majority Voting: Agreement-Based Clustering to Model Annotator Perspectives in Subjective NLP Tasks
Disagreement in annotation is a common phenomenon in the development of NLP datasets and serves as a valuable source of insight. While majority voting remains the dominant strategy for aggregating labels, recent work has…
Emotion ClassificationHate Speech DetectionSentiment AnalysisAffectSeek: Agentic Affective Understanding in Long Videos under Vague User Queries
Existing affective understanding studies have mainly focused on recognizing emotions from images, audio signals, or pre-cliped video clips, where the affective evidence is already given. This passive and clip-centered se…
Emotion ClassificationLow Resource Multimodal Translation of Nepali Spoken Words into Emotion-Conditioned Sign Language Avatars
Sign language communication systems, that integrate emotional expression remain underexplored, particularly for low-resource languages. This pilot study presents NEST-V1 (Nepali Emotion and Speech Transformer - Version 1…
Sign Language TranslationEmotion ClassificationEmotion RecognitionSpeech RecognitionBeyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation
Preserving affective nuance remains a challenge in Machine Translation (MT), where semantic equivalence often takes precedence over emotional fidelity. This paper evaluates the performance of three state-of-the-art Small…
Emotion ClassificationMachine TranslationBenchmarking PyCaret AutoML Against BiLSTM for Fine-Grained Emotion Classification: A Comparative Study on 20-Class Emotion Detection
Fine-grained emotion classification, which identifies specific emotional states such as happiness, anger, sadness, and fear, remains a challenging task in natural language processing. This study benchmarks classical mach…
Emotion ClassificationText ClassificationSentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking
Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment tools unreliable in practice. This paper describes a two-track classifi…
Emotion ClassificationMultilingual Multi-Label Emotion Classification at Scale with Synthetic Data
Emotion classification in multilingual settings remains constrained by the scarcity of annotated data: existing corpora are predominantly English, single-label, and cover few languages. We address this gap by constructin…
Emotion ClassificationA Hormone-inspired Emotion Layer for Transformer language models (HELT)
Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fundamentally lack the ability to process and respond to emotional context…
Emotion ClassificationResponse GenerationSentiment Analysis