Brand sentiment analytics plays a crucial role in the strategic decision-making processes of contemporary businesses. However, traditional sentiment analysis frameworks often rely heavily on structured data and neglect the rich insights available through unstructured social listening data. This paper proposes a comprehensive framework for integrating social listening data into brand sentiment analytics, focusing on real-time data ingestion, natural language processing, sentiment classification, and business intelligence interpretation. The study develops an end-to-end system architecture supported by empirical validations across multiple industries. The proposed framework demonstrates improved accuracy, contextual understanding, and predictive power in gauging brand sentiment.