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1. Introduction. In recent years, there has been a growing number of studies that have attempted to recognize human emotion from either speech [1,2,3], text [4,5], or facial expressions [6,7].In reality, emotional communication is a temporal and multimodal process; typical human conversations consist of a variety of cues and expressions that are rarely static.This project started by performing emotion detection. Emotivo is our name for the combination of the external Surrey Audio-Visual Expressed Emotion (SAVEE) database, the external RML Emotion Database, and the external Berlin Database of Emotional Speech. RML used 6 basic emotions: anger, disgust, fear, happiness, sadness, and surprise, while ...Contextual Emotion Detection in Text (DoubleDistilBert Model) We use the excellent pytorch-lightning, Huggingface transformers, and nbdev libraries for a clean and efficient implementation and demonstrate the performance of our model on the SemEval-2019 Task 3 dataset.Context-Aware Emotion Recognition Networks Jiyoung Lee1, Seungryong Kim2, Sunok Kim1, Jungin Park1, Kwanghoon Sohn1,∗ 1Yonsei University, 2Ecole Polytechnique F´ ed´ erale de Lausanne (EPFL)´ {easy00,kso428,newrun,khsohn}, [email protected] Abstract Traditional techniques for emotion recognition have fo-< span > Facial Emotion Recognition was a summer project under BCS, IITK with the aim to implement an Emotion Recognition Model and test their accuracy for several datasets available like CK+, FER2013, and JFFE. </ span >Emotion Detection 😠🤢😱😇😐☹️😲 CNN using keras. We will use 7 emotions namely - We have use 7 emotions namely - 'Angry'😠, 'Disgust'🤢, 'Fear'😱, 'Happy'😇, 'Neutral'😐, 'Sad'☹️, 'Surprise'😲 to train and test (validation) our algorithm using Convolution Neural Networks. You can directly use kaggle kernel ...