Social Sensing for Sentiment Analysis of Policing Authority Performance in Smart Cities - Malik, Tariq; Tahir, Ahsen; Bilal, Ahsan; Dashtipour, Kia; Imran, Muhammad Ali; Abbasi, Qammer H. (Frontiers in Communications and Networks, 2022)
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 use of smart phones, cameras etc. We perform sentiment analysis and mine public opinion on 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 8000 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 PSCA on Facebook, Twitter, Youtube and Web TV. Sentiment analysis of local civil services is not possible without taking into account the local language. In this paper, we utilise machine learning techniques to perform multi-class sentiment analysis of public opinion on policing authority and the provided civil services in both the local language 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 overtime from a period of 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.