This report details a knowledge engineering project focused on analyzing Twitter data using the RapidMiner platform. The project aims to evaluate user activities and extract meaningful insights from tweets. The methodology includes three primary knowledge creation techniques: classification using decision trees, clustering via Self-Organizing Maps (SOM), and association rule mining with the FP Growth algorithm. The report outlines the attributes used, the processes involved in each technique, and the performance evaluation metrics such as Accuracy and Kappa statistics. The decision tree model classifies tweets, while SOM visualizes the data by reducing dimensionality. The FP Growth algorithm identifies frequent itemsets and association rules. The report presents detailed steps, visualizations, and the outcomes for each technique, demonstrating how these methods can be applied to analyze social media data and understand user behavior. The project provides a practical application of data mining and knowledge discovery principles within the context of a real-world social media platform, Twitter.
![[object Object]](/_next/static/media/star-bottom.7253800d.svg)
