PARSING OF SOCIAL MEDIA FEEDS: AN ADAPTIVE FRAMEWORK

Abstract: Machine learning classification and natural language processing play a significant role in extracting insights from high-velocity social media datasets. Social media platforms generate vast quantities of dynamic, unstructured content that can inform trend tracking, sentiment analysis, and social research. However, systematically ingesting and parsing these streams presents substantial challenges due to restrictive platform interfaces, structural schema drift, and high lexical noise in colloquial microblog text. This paper presents an adaptive architectural framework designed to integrate data collection, multi-stage text preprocessing, named entity extraction, and schema normalization into a unified, modular pipeline. By coupling asynchronous queue buffering with staged text cleaning and domain-adapted language representations, the proposed design aims to balance processing efficiency with semantic fidelity across heterogeneous platforms. The architecture distinguishes conceptual system design from empirical validation, establishing a principled foundation and evaluation methodology for future experimental benchmarking in streaming social intelligence systems.

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