A FRAMEWORK FOR TWEET CLASSIFICATION AND ANALYSIS ON SOCIAL MEDIA PLATFORM USING FEDERATED LEARNING.

This article presents a framework for classifying and analyzing tweets on social media platforms using Federated Learning (FL). The framework incorporates feature mapping and feature indexing techniques to determine the threshold computation value for categorizing tweets as either "positive" or "negative." It is platform-agnostic and has been validated using a diverse dataset from platforms like X (formerly known as Twitter), Koo, and Instagram. The framework achieved an accuracy rate of 93.54% in classifying trending topic posts. This research highlights the significance of social media and its role in data analysis, particularly through the lens of FL. [Extracted from the article]