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Contact tracing has been globally adopted in the fight to control theinfection rate of COVID-19. Thanks to digital technologies, such as smartphonesand wearable devices, contacts of COVID-19 patients can be easily traced andinformed about their potential exposure to the virus. To this aim, severalinteresting mobile applications have been developed. However, there areever-growing concerns over the working mechanism and performance of theseapplications. The literature already provides some interesting exploratorystudies on the community's response to the applications by analyzinginformation from different sources, such as news and users' reviews of theapplications. However, to the best of our knowledge, there is no existingsolution that automatically analyzes users' reviews and extracts the evokedsentiments. In this work, we propose a pipeline starting from manual annotationvia a crowd-sourcing study and concluding on the development and training of AImodels for automatic sentiment analysis of users' reviews. In total, we employeight different methods achieving up to an average F1-Scores 94.8% indicatingthe feasibility of automatic sentiment analysis of users' reviews on theCOVID-19 contact tracing applications. We also highlight the key advantages,drawbacks, and users' concerns over the applications. Moreover, we also collectand annotate a large-scale dataset composed of 34,534 reviews manuallyannotated from the contract tracing applications of 46 distinct countries. Thepresented analysis and the dataset are expected to provide a baseline/benchmarkfor future research in the domain.

Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset