%0 Generic %1 ahmad2021sentiment %A Ahmad, Kashif %A Alam, Firoj %A Qadir, Junaid %A Qolomany, Basheer %A Khan, Imran %A Khan, Talhat %A Suleman, Muhammad %A Said, Naina %A Hassan, Syed Zohaib %A Gul, Asma %A Al-Fuqaha, Ala %D 2021 %K Analysis Benchmark COVID-19 Dataset Sentiment %T Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset %U http://arxiv.org/abs/2103.01196 %X Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In total, we employ eight different methods achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. We also highlight the key advantages, drawbacks, and users' concerns over the applications. Moreover, we also collect and annotate a large-scale dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The presented analysis and the dataset are expected to provide a baseline/benchmark for future research in the domain.

@misc{ahmad2021sentiment, abstract = {Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In total, we employ eight different methods achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. We also highlight the key advantages, drawbacks, and users' concerns over the applications. Moreover, we also collect and annotate a large-scale dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The presented analysis and the dataset are expected to provide a baseline/benchmark for future research in the domain.}, added-at = {2021-03-31T22:21:45.000+0200}, author = {Ahmad, Kashif and Alam, Firoj and Qadir, Junaid and Qolomany, Basheer and Khan, Imran and Khan, Talhat and Suleman, Muhammad and Said, Naina and Hassan, Syed Zohaib and Gul, Asma and Al-Fuqaha, Ala}, biburl = {https://www.bibsonomy.org/bibtex/29f353bee2811ad31e1a720fb1e525faf/firojalam}, description = {Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset}, interhash = {84f30188d999a447770811a4e56d47fd}, intrahash = {9f353bee2811ad31e1a720fb1e525faf}, keywords = {Analysis Benchmark COVID-19 Dataset Sentiment}, note = {cite arxiv:2103.01196Comment: 11 pages, 6 figures}, timestamp = {2021-03-31T22:21:45.000+0200}, title = {Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset}, url = {http://arxiv.org/abs/2103.01196}, year = 2021 }