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Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language

Kashif Ahmad¹, Firoj Alam², Junaid Qadir³

¹Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

JMIR Formative Research

|April 7, 2022

Summary

This summary is machine-generated.

This study introduces an AI-powered framework for analyzing user reviews of COVID-19 contact tracing apps. The system accurately extracts sentiments, offering a rapid surveillance tool for app development.

Keywords:

BERT COVID-19 NLP RoBerta contact tracing applications fastText sentiment analysis text classification transformers

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Area of Science:

Artificial Intelligence
Natural Language Processing
Public Health Technology

Background:

COVID-19 contact tracing apps are widely used, but their effectiveness and user reception require continuous monitoring.
Existing methods for analyzing user feedback are manual and time-consuming.
There is a need for automated solutions to rapidly assess user sentiment towards these critical health applications.

Purpose of the Study:

To evaluate the efficacy of AI and NLP techniques for automated sentiment analysis of COVID-19 contact tracing app reviews.
To develop and validate a sentiment analysis framework for classifying user review polarity.
To create a large-scale annotated dataset for advancing research in this domain.

Main Methods:

A pipeline involving manual annotation of user reviews through crowdsourcing.
Development and training of AI models, including classical and deep learning approaches, for sentiment classification.
Collection and annotation of a substantial dataset of user reviews for COVID-19 contact tracing applications.

Main Results:

Achieved high performance with an average F1 score of 94.8% across 8 methods and 3 tasks, demonstrating the feasibility of the proposed solution.
Generated a large-scale benchmark dataset comprising 34,534 manually annotated user reviews.
Successfully developed a proof-of-concept web application for rapid sentiment analysis.

Conclusions:

AI and NLP techniques effectively analyze and classify user sentiments in app reviews, outperforming manual methods in speed and accuracy.
The developed framework and benchmark dataset can serve as a rapid surveillance tool for monitoring mobile applications during emergencies.
Automated sentiment analysis enables quicker identification of app issues and facilitates timely improvements without intensive user design processes.