PARSING OF SOCIAL MEDIA FEEDS: AN ADAPTIVE FRAMEWORK

Abstract: Machine learning supervised classification and text processing play a significant role in handling large social media datasets. Social media sites produce huge amounts of data on a daily basis, which is why they are good sources of information to understand public trends. However, automatically extracting useful information from these feeds is challenging as the data is noisy, fast-moving, and formatted differently across platforms. This research presents an automated system for collecting, cleaning, and organizing live social media posts into structured formats. We process incoming posts in real-time using a combination of NLP text-cleaning techniques and direct platform integrations. Live data stream testing results show the system is highly accurate and cuts processing time significantly from traditional methods. This solution provides a reliable basis for researchers who need real-time data for analysis.

Keywords: Social Media Parsing, Natural Language Processing, API Ingestion, Text Classification, Data Normalization.