This study presents an in-depth analysis of community complaints directed at public services on Twitter. With the rapid growth of social media as a platform for public discourse, understanding and categorizing these complaints is crucial for governments and service providers to improve responsiveness and service quality. Leveraging natural language processing techniques, sentiment analysis, and machine learning, we examine a diverse range of Twitter complaints, classify them into specific categories, and provide insights into the most common issues faced by communities when interacting with public services on this platform. The findings offer valuable guidance for enhancing public service delivery and engagement in the digital age.