SentimentPulse: A Concurrent Multi-Platform System for Social Media Sentiment Monitoring with LLM-Based Interpretation

Monitoring public perception on social media is increasingly important for detecting reputational risks and communication opportunities in rapidly evolving digital environments. However, operational sentiment monitoring remains challenging due to platform fragmentation, heterogeneous data formats, and the need to generate interpretable reports quickly when specialized analysts are not available. This study presents SentimentPulse, a web-based system for multi-platform sentiment monitoring driven by a user-defined query. The system integrates concurrent data extraction from X, Facebook, LinkedIn, and Instagram with large language model (LLM) based sentiment classification and automated executive storytelling generation. The architecture combines parallel scraping processes for data acquisition with multithreaded LLM inference to improve throughput, while structured persistence enables job tracking and cross-platform analysis. The system operates under practical constraints, including platform-specific access limitations, dynamic content availability, and dependency on external LLM services, which may introduce variability in response times and outputs. The evaluation is conducted under controlled experimental conditions using fixed query limits and asynchronous execution settings, and results should be interpreted within these operational boundaries. Experimental results from two anonymized case studies demonstrate the effectiveness and operational performance of the approach. In the first case study, the system processed 1032 social media interactions and produced a sentiment distribution of 49.0% positive, 36.6% negative, and 14.2% neutral, with a manual validation accuracy of 0.88. In the second case study, the pipeline processed 1121 records, with parallel scraping accounting for the majority of the runtime and LLM inference achieving a throughput of 12.08 items per second. These results show that combining concurrent multi-platform extraction with LLM-based interpretation enables practical and interpretable social listening workflows, while highlighting the importance of considering system-level constraints when deploying such solutions in real-world environments.