Papers Emotion Classification
“Emotion Classification” 태그가 달린 논문 540편 · 필터 해제
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach
Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping …
EEGEmotion ClassificationEmotion RecognitionImproved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems. This paper introduces a method for categorizing emotions fro…
ClassificationEmotion ClassificationOrdinal ClassificationRisk prediction of pathological gambling on social media
This paper addresses the problem of risk prediction on social media data, specifically focusing on the classification of Reddit users as having a pathological gambling disorder. To tackle this problem, this paper focuses…
Emotion ClassificationPredictionSensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is unden…
ClassificationEmotion ClassificationEmotion Detection with Transformers: A Comparative Study
In this study, we explore the application of transformer-based models for emotion classification on text data. We train and evaluate several pre-trained transformer models, on the Emotion dataset using different variants…
Emotion ClassificationAn Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification
Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machi…
DenoisingEmotion ClassificationSensitivityStochastic OptimizationPersian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification
The lack of a suitable tool for the analysis of conversational texts in the Persian language has made various analyses of these texts, including Sentiment Analysis, difficult. In this research, we tried to make the under…
Emotion ClassificationSentiment AnalysisSentiment ClassificationEmotion Classification in Low and Moderate Resource Languages
It is important to be able to analyze the emotional state of people around the globe. There are 7100+ active languages spoken around the world and building emotion classification for each language is labor intensive. Par…
ClassificationCross-Lingual TransferEmotion ClassificationTransfer LearningEmotion Classification in Short English Texts using Deep Learning Techniques
Detecting emotions in limited text datasets from under-resourced languages presents a formidable obstacle, demanding specialized frameworks and computational strategies. This study conducts a thorough examination of deep…
Deep LearningEmotion ClassificationTransfer LearningGiMeFive: Towards Interpretable Facial Emotion Classification
Deep convolutional neural networks have been shown to successfully recognize facial emotions for the past years in the realm of computer vision. However, the existing detection approaches are not always reliable or expla…
ClassificationEmotion ClassificationEmotion RecognitionFacial Emotion RecognitionHandling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation
The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when en…
ClassificationEmotion ClassificationOnly My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models
Deep neural networks are extensively applied to real-world tasks, such as face recognition and medical image classification, where privacy and data protection are critical. Image data, if not protected, can be exploited …
Adversarial AttackClassificationEmotion ClassificationFace Recognition+5Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification
Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data. Pre-trained language models (PLMs) have achieved high…
Emotion ClassificationEnglish Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of emotion categories is highly domain-spec…
Emotion ClassificationEmotion RecognitionLanguage ModellingLarge Language Model+1TONE: A 3-Tiered ONtology for Emotion analysis
Emotions have played an important part in many sectors, including psychology, medicine, mental health, computer science, and so on, and categorizing them has proven extremely useful in separating one emotion from another…
Emotion ClassificationEmotion RecognitionA Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
In terms of human-computer interaction, it is becoming more and more important to correctly understand the user's emotional state in a conversation, so the task of multimodal emotion recognition (MER) started to receive …
ClassificationContrastive LearningEmotion ClassificationEmotion Recognition+1Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian Texts
The identification of emotions in short texts of low-resource languages poses a significant challenge, requiring specialized frameworks and computational intelligence techniques. This paper presents a comprehensive explo…
Deep LearningDimensionality ReductionEmotion ClassificationTransfer LearningTopic Bias in Emotion Classification
Emotion corpora are typically sampled based on keyword/hashtag search or by asking study participants to generate textual instances. In any case, these corpora are not uniform samples representing the entirety of a domai…
ClassificationEmotion ClassificationLearning Arousal-Valence Representation from Categorical Emotion Labels of Speech
Dimensional representations of speech emotions such as the arousal-valence (AV) representation provide a continuous and fine-grained description and control than their categorical counterparts. They have wide application…
Dimensionality ReductionEmotion ClassificationregressionSpeech Synthesis+3Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models
The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the rel…
Emotion Classification