Emotion Recognition
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ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.
3D FACIAL EXPRESSION RECOGNITION EMOTION RECOGNITION FACIAL LANDMARK DETECTION
Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI).
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
EMNLP 2018
• SenticNet/conv-emotion
• 
Emotion recognition in conversations is crucial for building empathetic machines.
#2 best model for
Emotion Recognition in Conversation
on IEMOCAP
EMOTION RECOGNITION IN CONTEXT EMOTION RECOGNITION IN CONVERSATION MULTIMODAL EMOTION RECOGNITION MULTIMODAL SENTIMENT ANALYSIS
Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos
NAACL 2018
• SenticNet/conv-emotion
•
Emotion recognition in conversations is crucial for the development of empathetic machines.
#3 best model for
Emotion Recognition in Conversation
on IEMOCAP
EMOTION RECOGNITION IN CONTEXT EMOTION RECOGNITION IN CONVERSATION OPINION MINING
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
Several lexica for sentiment analysis have been developed and made available in the NLP community.
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
• tzirakis/Multimodal-Emotion-Recognition
•
The system is then trained in an end-to-end fashion where - by also taking advantage of the correlations of the each of the streams - we manage to significantly outperform the traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Context-Dependent Sentiment Analysis in User-Generated Videos
ACL 2017 • soujanyaporia/multimodal-sentiment-analysis
Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos.
MULTIMODAL EMOTION RECOGNITION MULTIMODAL SENTIMENT ANALYSIS NAMED ENTITY RECOGNITION SARCASM DETECTION
EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis
We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format.