Uncovering the Limits of Text-based Emotion Detection
Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages. We design a benchmark and evaluate several feature spaces and learning algorithms, including two simple yet novel models on top of BERT that outperform previous strong baselines on GoEmotions. Through an experiment with human participants, we also analyze the differences between how writers express emotions and how readers perceive them. Our results suggest that emotions expressed by writers are harder to identify than emotions that readers perceive. We share a public web interface for researchers to explore our models.
Code (2)
Tasks
Emotion ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help i…
Medical DiagnosisReal-Time Multi-Modal Embedded Vision Framework for Object Detection Facial Emotion Recognition and Biometric Identification on Low-Power Edge Platforms
Intelligent surveillance systems often handle perceptual tasks such as object detection, facial recognition, and emotion analysis independently, but they lack a unified, adaptive runtime scheduler that dynamically alloca…
Facial Emotion RecognitionEmotion ClassificationFace RecognitionObject DetectionWhy Are Some Emotions Harder for LLMs? Uncovering the Causal Mechanisms of Emotion Inference via Sparse Autoencoders
Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, where reliable emotion detection is essential. However, their emotion recognition abilities remain uneven: models often p…
Emotion RecognitionEmoAttack: Emotion-to-Image Diffusion Models for Emotional Backdoor Generation
Text-to-image diffusion models can generate realistic images based on textual inputs, enabling users to convey their opinions visually through language. Meanwhile, within language, emotion plays a crucial role in express…
Backdoor AttackDiffusion PersonalizationImage GenerationLeveraging Large Language Models to Analyze Emotional and Contextual Drivers of Teen Substance Use in Online Discussions
Adolescence is a critical stage often linked to risky behaviors, including substance use, with significant developmental and public health implications. Social media provides a lens into adolescent self-expression, but i…