Emotion AI (Sentiment Analysis) of Tweets using TextBlob and Django (Python)

An emotion-driven optimizer that feels loss and navigates accordingly.

An emotion-driven optimizer that feels loss and navigates accordingly.

AI-first subjectivity kernel for agents — persistent emotional state, relation dynamics, adaptive reply loops. Zero extra LLM calls.

Emotion-aware AI companion that uses facial expressions, voice tone, and chat sentiment to generate empathetic responses — all running offline on your device.

Real-time emotion recognition with 40-channel EEG, facial analysis & PPG fusion - PyQt6 interface with DEAP dataset, KNN/SVM classifiers

Sentiment Analysis Visualization

🎤 Enhance speech recognition by detecting emotions in spoken language, combining OpenAI's Whisper and emotion analysis for deeper insights.

NamoNexus v1.0 - Emotion-aware AI Dashboard with Real-time Visualization

NVatar — AI Avatar Chat System. Fully local Gemma 26B + Claude hybrid. Emotion, memory, personality evolution, 3D VRM avatars.

A deep learning project for generating emotionally expressive music. Using Transformer and GAN models, it enables user-controlled music creation based on specified emotions. Aims to produce realistic, human-like melodies

一个有内在生活的虚拟人格 agent — memory, emotions, subjective relationships and a real daily rhythm

Real-time facial emotion recognition from webcam

Mood Meets Media

C# SDK for the Hume AI API -- emotion recognition and analysis from audio, video, text, and images

Enhanced facial emotion recognition with CNN-BiLSTM

Epistemological foundation of the JiaYin Affective Computing System, providing the framework for AI emotional decision-making under uncertainty.

Minimalist emotion model for AI agents using only desire and anxiety.

Validates whether META's TRIBE v2 predicted brain activations help LLMs understand emotion. 3-condition A/B/C experiment, 100 samples, ElevenLabs audio + local LLM inference.

An intelligent speech recognition system that combines OpenAI's Whisper for accurate transcription with dual emotion detection models. Analyzes both audio characteristics (tone, pitch, intensity) and textual content to provide comprehensive emotional context alongside transcriptions.