Analysis of user's car parking behaviour through twitter hashtags

Abstract

Due to its ease and popularity, social media has recently become an essential source of data for researchers and various stakeholder groups that seek a reliable assessment of their policies based on a comprehensive understanding of users' feedback and inputs, which are reflected in their posts and discussions. In this study, we investigate the issue of users' car parking behaviour through a comprehensive analysis of related hashtags collected from the Twitter social media platform. For this purpose, we adopted a two‐step strategy where in the first stage, a surface‐level analysis of the identified hashtags involving inductive reasoning, sentiment analysis, and user interaction in terms of engagement and diversity scores is performed. In the second phase, a tweet content analysis is performed using sentiment analysis and Empath categorization with respect to the most frequent wordings (assimilating to separate topics), in the same spirit as aspect‐sentiment analysis, to gain further insights regarding the occurrence of negative and positive posts. A quantitative evaluation of the coherence of the Empath categorization indicates that achieved 0.83% coherence score and outperformed both LDA and LSA that had a score of 0.77% and 0.76% respectively. Furthermore, the common word technique assimilated to aspect sentiment compared to the state‐of‐the‐art model for aspect sentiment‐based deberta‐v3‐base model. Besides, the influence of bots or spammers is evaluated using engagement/diversity measures and Botometer API. The results provide valuable insights in terms of discriminating between positive and negative posts and the correlation of surface‐level analysis with content‐based analysis, as well as the impact of various categorizations. The results expect to enable urban planners and policymakers to advance evidence‐based policing in the future design of intelligent parking systems.