RETRACTED CHAPTER: Unveiling an Effective Framework for Extracting and Evaluating User Opinions on Public Transportation Services Through Twitter: A Case Study of Delhi Metro

User satisfaction plays a vital role in the success of any public transport system. Recent studies in the field of social science have emphasized the significance of extracting valuable information from social media platforms to track and analyse user behaviour. Public participation is crucial in decision-making processes, and the use of social media data has emerged as a valuable tool for engaging public transport users through the internet, smartphone apps, and social media platforms. This paper aims to explore the feasibility of utilizing Twitter data to evaluate user perceptions of the Delhi metro, presenting a comprehensive methodology for extracting, processing, and interpreting the collected data. The research focuses on the Delhi metro as a pilot case, utilizing user satisfaction surveys. This study proposes an innovative approach as an addition to the existing structure, providing valuable insights for updating feedback data. Data collection involves extracting user feedback from social media posts, particularly tweets. A comprehensive framework is presented to efficiently extract and analyse user opinions on transportation services from Twitter. The development and validation process of the framework are illustrated through a case study specifically focusing on the Delhi Metro. The methodology comprises several steps. Initially, Twitter data is collected and pre-processed from a specific area and time to eliminate erroneous and redundant information. Text classification models, trained on manually labelled datasets, are then applied to filter Twitter data related to personal opinions on transportation services. Additionally, topic modelling and tokenization techniques are employed to extract relevant semantic content necessary for data analysis. This research proposes a framework based on social media data to capture the satisfaction of a large user base of public transport, enabling the improvement of user satisfaction characterization and location. Moreover, the study identifies potential sources of social network-related data that can be utilized in transport planning, discussing their advantages and limitations. In conclusion, this study highlights the potential of utilizing Twitter data for evaluating user perceptions of the Delhi metro. The comprehensive framework presented enables efficient extraction and analysis of user opinions on transportation services. By leveraging social media data, public transport systems can better understand user satisfaction and enhance their services accordingly. Additionally, the research identifies the opportunities and limitations of utilizing social network-related data for transport planning purposes. This study contributes to the field by demonstrating the value of social media data in improving public transport systems and informing decision-making processes.