Public Perception of RapidKL Service Using Twitter Sentiment Analysis

This study investigates public perception of the Rapid KL service through sentiment analysis of user-generated content on Twitter. With the increasing reliance on social media as a platform for expressing opinions and feedback, understanding sentiment trends is crucial for service improvement and community engagement. Utilizing advanced sentiment analysis techniques, including machine learning algorithms and natural language processing, this research analyzes a dataset of comments and posts related to the Rapid KL service. The findings reveal significant insights into user satisfaction, areas for improvement, and the overall sentiment landscape surrounding the service. By identifying prevalent themes and sentiments, this study aims to provide actionable recommendations for stakeholders to enhance service delivery and foster positive community relations. The implications of this research extend to broader applications in public service management and social media engagement strategies.