Emotion-Based Content Recommendation - Emorec

Overview

This repository contains the implementation of an innovative Emotion-Based Content Recommendation System. The project adopts a multifaceted approach, integrating various technologies to deliver a seamless and personalized content recommendation experience. The core components include a Python Flask backend, a ReactJs frontend, and a chatbot functionality powered by the OpenAI library. The recommendation engine leverages a Bi-LSTM model trained on emotion-labeled datasets, utilizing popular Python libraries such as numpy, scikit-learn, TensorFlow, and pandas.

Technologies Used

Backend: Python Flask

Frontend: ReactJs

Chatbot Functionality: OpenAI Library

Recommendation Engine: Bi-LSTM model, numpy, scikit-learn, TensorFlow, pandas

Version Control: GitHub