Paper Contents
Abstract
In recent years, social media platforms have become a significant source of information, reflecting public opinion, trends, and real-time events. Parsing social media feeds is a critical process in data mining and analytics, aimed at extracting structured information from the vast, unstructured data generated by users. This research paper explores the challenges, methods, and tools used in parsing social media feeds. It reviews various approaches including rule-based methods, machine learning, and hybrid models. A case study demonstrates the practical implementation of parsing techniques on sample Twitter data. Furthermore, the paper discusses existing challenges such as handling multilingual content, noisy data, and real-time processing limitations. Finally, it suggests future directions to enhance parsing efficiency and accuracy in social media analytics.
Copyright
Copyright © 2025 Miss. Sayali P. Chopade. This is an open access article distributed under the Creative Commons Attribution License.
