Abstract

This study focuses on developing a comprehensive sentiment analysis framework aimed at understanding sentiments expressed in social media posts, enhancing online reputation management for brands. Given the overwhelming volume of user-generated content across platforms, we instituted a methodical approach leveraging advanced machine learning techniques. Specifically, we used Python libraries such as TensorFlow for deep learning functionalities and PyTorch for natural language processing tasks. Our models classify sentiments into three categories: positive, negative, and neutral, while simultaneously analyzing trending patterns and user opinions. The results demonstrate a significant classification accuracy, providing evidence for the framework’s efficacy in real-time social media monitoring. Ultimately, our findings emphasize the strategic role of sentiment analysis in improving brand reputation, enabling better consumer engagement, and informing marketing strategies.