An LSTM-based sentiment analysis model for classifying text emotions. Built with deep learning techniques to accurately detect and predict sentiment in text data.

This project is a basic emotion recognition system that combines OpenAI's GPT API and a deep learning model trained on the FER2013 dataset. It detects facial emotions in real-time from a webcam feed and generates AI responses based on the user's emotion. The project is implemented using TensorFlow, OpenCV, and OpenAI's API

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

Text emotions classification is the problem of assigning emotion to a text by understanding the context and the emotion behind the text. One real-world example is the keyboard of an iPhone that recommends the most relevant emoji by understanding the text.

AI-powered wellness journaling platform using BERT emotion analysis and psychosomatic body mapping. Fine-tuned on 58k emotions dataset with adaptive detection (1-5 emotions per entry). Real-time visualization of mind-body connections through interactive anatomical mapping. Dual-server architecture with Next.js frontend and Python ML backend.

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.

Real-time facial emotion recognition using ResNet50 (65.59% accuracy on FER2013 dataset). Detects 7 emotions via webcam with PyTorch, MediaPipe face detection, FP16 GPU optimization,

Moodix — локальный модуль анализа русскоязычного текста, определяющий основное настроение (позитивное, нейтральное, негативное), 16 суб-настроений и 6 деструктивных признаков (угроза, ненависть, экстремизм и др.). Основан на BiLSTM-модели и работает без доступа к интернету. Подходит для интеграции в CRM, e-commerce, модерации и аналитики.

A powerful FastAPI-based REST API for real-time audio emotion detection. Uses state-of-the-art machine learning models from Hugging Face to analyze audio files and identify emotional content such as happiness, sadness, anger, and more. Built with Python, featuring automatic audio resampling, confidence scoring, and comprehensive API documentation.

🎭 Identify and analyze facial emotions in real time using a high-performance ResNet50 model trained on the FER2013 dataset.