An emotion classifier of text containing technical content from the SE domain

GiMeFive: Towards Interpretable Facial Emotion Classification 😄😲😭😡🤢😨 (PyTorch Implementation)

Mobile Messanger Application with Automatic Facial Emotion Classification

Predict emotions (happiness, anger, sadness) from WhatsApp chat data using machine learning and deep learning models. Includes text normalization, vectorization (TF-IDF, BoW, Word2Vec, GloVe), and model evaluation.

Official code repository for paper "Multi-modal Speech Emotion Recognition using Multi-head Attention Fusion of Multi-feature Embeddings". Paper accepted to EAI INISCOM 2023

The detection of emotion is made by using the machine learning concept. You can use the trained dataset to detect the emotion of the human being. For detecting the different emotions, first, you need to train those different emotions, or you can use a dataset already available on the internet.

Real-time Emotion Recognition using Physiological signals in e-Learning Here one can find the development of realtime emotion recognition using various physiological signals

Project Babble Module for VRCFaceTracking v5. An open-source VR mouth tracking solution

3DiVi Face SDK is a set of software components (code libraries) for building face recognition solutions

Unofficial implementation of the EmotionROI essay and extended applications

Show me how do I feel now.. 😄😲🤢😨😭😡

The diploma and research project focuses on exploring the correlation between emotion classification and head pose orientation.

A webapp meant to trace and to monitor the feelings we're feeling daily

Text Emotion System Sentiment Analysis (TESSA) is an open-source project focused on sentiment analysis. 💬

Original dissertation title: "Extract Emotional Tags from Movie Synopses". The identification, definition, and automatic prediction of a set of emotions in movies, based on their movie abstracts and various metadata.

A Docker-Based Federated Learning Framework Design and Deployment for Multi-modal Data Stream Classification

The inference of emotions by a probabilistic programming language account for both the overall emotional responses and individual emotions for different outcomes.

Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data