Optimizing Social Media Engagement through Effective Tag Suggestion and Engagement Prediction

This research develops an intelligent system for optimizing social media content using Machine Learning and data analytics. It features a novel tag recommendation model that leverages Natural Language Processing (NLP) for text and Computer Vision (CV) for images, analyzing historical data and user behavior to suggest optimal tags. The system also includes an engagement prediction model (using XGBoost), accurately forecasting post-engagement scores. Explainable AI (XAI) techniques (SHAP and LIME) are integrated to provide transparency for the tag choices.