Structuring Social Media Feeds for Intelligent Analysis

In the digital age, social media platforms have become prolific sources of real-time information. However, the nature of social media data—unstructured, multilingual, and dynamic—poses significant challenges to data parsing and analysis. This paper presents an approach for parsing social media feeds to extract structured, meaningful, and actionable insights. Using a combination of natural language processing (NLP), metadata extraction, and stream processing techniques, the system efficiently transforms unstructured social media posts into categorized and analyzable data. The proposed framework is capable of handling text normalization, named entity recognition, sentiment detection, and keyword extraction in real-time, making it suitable for applications such as event monitoring, market analysis, and crisis detection. The paper also discusses implementation strategies, results from live Twitter data, and the broader implications of deploying such systems in scalable, privacy-conscious environments