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

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.

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.