The rapid evolution of social media has transformed public opinion analysis, offering valuable insights for urban governance. This study examines the Bruhat Bengaluru Mahanagara Palike (BBMP), Bengaluru’s municipal body, using a multidimensional analysis of Twitter data to address gaps in leveraging social media for citizen feedback in the Indian context. Employing sentiment analysis, thematic analysis, and word frequency analysis, the research uncovers public concerns and highlights governance challenges. The study reveals that 61.5% of tweets were neutral, while 27% expressed negative sentiment, and only 11.5% were positive, indicating significant dissatisfaction with infrastructure, corruption, and urban management. Key themes include “crumbling roads” (154 references) and “storm drain deaths,” with notable mentions of corruption (e.g., “Bribe 50 K”). The research methodology integrates Python’s Tweepy library for data collection and NVIVO for sentiment and thematic analysis, achieving 93.4% accuracy. Compared to similar studies, BBMP’s analysis shows a higher prevalence of negative sentiment, emphasizing the need for actionable governance reforms. This study demonstrates that social media analytics can provide policymakers with real-time feedback to enhance public engagement, improve transparency, and address critical issues, ultimately fostering a more responsive and citizen-focused governance framework.