Abstract
High-tech services in smart cities, ubiquity of smart phones, and proliferation of social media platforms have enabled social sensing, either through direct human observers or through humans as sensor carriers and operators, such as through the use of smart phones, cameras, etc. We performed a sentiment analysis (SA) and mined public opinion on the civil services and policing authority in a smart city. The establishment of high-tech policing in Lahore, Pakistan, known as the Punjab Safe Cities Authority (PSCA), Lahore, along with integrated command and control centers and various equipments, such as 8,000 cameras, monitoring sensors, etc., has resulted in a requirement for its performance evaluation and social media–enabled opinion mining to determine the broader impact on communities. Social sensing of civil services has been enabled through the presence of the PSCA on Facebook, Twitter, YouTube, and Web TV. The SA of the local civil services is not possible without taking into account the local language. In this article, we utilize machine learning techniques to perform multi-class SA of public opinion on policing authority and the provided civil services in both the local languages Urdu and English. The support vector machine provides the highest performance multi-classification accuracy of 86.87% for positive, negative, and neutral sentiments. The temporal sentiments are determined over time from January 2020 to July 2021, with an overall positive sentiment of 62.40% and a negative sentiment of 13.51%, which shows high satisfaction of policing authority and the provided civil services.
Highlights
The internet provides a large repository of structured and unstructured data
Data are collected from the social media platforms, the data selected from videos on YouTube that are projecting service delivery on behalf of the Punjab Safe Cities Authority (PSCA)
The positive public sentiment indicates higher satisfaction with the services provided by the civil services
Summary
The internet provides a large repository of structured and unstructured data. The analysis of data to extract potential and relevant information is a challenge. Social media platforms like Twitter, Facebook, LinkedIn, etc. Have assumed an important role in the expression of public opinion toward various events, news, and performance of the government, civil services, and public organizations. Many civil services and organizations are utilizing social media as a tool for publicizing products and services. Social media enables the collection and analysis of their clients’ feedback, comments, and reviews for improvement of the service provisions. Social sensing of civil services has become an important tool and a requirement in future smart cities for enabling near-real-time feedback and proper management of services
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