Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R. and Hassanien, A. E. (2020) Sentiment Analysis of COVID-19 Tweets by Deep Learning Classifiers—A Study to Show How Popularity Is Affecting Accuracy in Social Media. Applied Soft Computing, 97, Article ID: 106754.
https://doi.org/10.1016/j.asoc.2020.106754

has been cited by the following article:

• TITLE: An Analytical Framework for Measuring Inequality in the Public Opinion on Policing—Assessing the Impacts of COVID-19 Pandemic Using Twitter Data

  AUTHORS: Monsuru Adepeju, Fatai Jimoh

  KEYWORDS: Inequality, Policing, COVID-19 Pandemic, Opinion Analysis, Visualization

  JOURNAL NAME: Journal of Geographic Information System, Vol.13 No.2, March 19, 2021

  ABSTRACT: As the COVID-19 pandemic sweeps across the globe, police forces are charged with new roles as they engage and enforce new policies and laws governing societal behaviours. However, how the police exercise these powers is an important factor in shaping public opinion and confidence concerning their activities across space and time. This research developed an analytical framework for measuring the inequality in the public opinion towards policing efforts during the pandemic using Twitter data. We demonstrate the utility of our framework using 3-months of tweets across 42 police force areas (Pfas) of England and Wales (UK). The results reveal that public opinions on policing is overwhelmingly negative across space and time, and that these opinions have been most exacerbated by the COVID-19 pandemic in three specific Pfas, namely Staffordshire, Thames Valley, and North Wales. We provided the link to the open-source script by which this research could be replicated and adapted to other study areas. This research has the potential to help law enforcement understand the dynamics of public confidence and trust in policing and facilitate action towards improved police services.