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

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.

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.

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.

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

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