Predicting LinkedIn Post Reactions: A Comparative Analysis of Machine Learning Models

Social media platforms have become a critical space for businesses to interact with their audience. LinkedIn, a professional networking platform, offers metrics like reactions, comments, and shares to gauge post engagement. This paper investigates a data-driven approach for predicting the number of responses to a LinkedIn post. Various features such as post content, company statistics (e.g., followers and connections), and media type are analyzed using machine learning models. The study compares the performance of multiple regression models and explores the importance of features, yielding valuable insights for optimizing LinkedIn post engagement.