DECIPHERING USER FEEDBACK: CLUSTERING OF DIGITAL WELL-BEING SYSTEM REVIEWS

Abstract

This research holds significant importance in the realm of digital well-being, addressing the increasing concern over digital addiction. By analyzing user feedback on digital well-being systems, the study provides valuable insights into the effectiveness of these systems in moderating digital habits. It contributes to understanding how digital detox applications can mitigate compulsive smartphone use, thereby enhancing mental health. The study stands out for its unique approach to clustering user feedback on two popular digital well-being systems, Space and Google Well-being app. This original approach provides a novel perspective on user interaction with digital well-being tools, distinguishing it from existing literature which primarily focuses on general usage patterns and the impact on mental health. The purpose of this research is to systematically analyze and cluster user feedback on digital well-being systems. It also incorporates information retrieval techniques like topic modeling to extract main topics from these reviews. This methodology allows for an in-depth understanding of user feedback, going beyond mere statistical analysis to capture the nuances of user experiences and expectations. The findings of this research have significant implications for software developers and stakeholders in the digital well-being domain.

Keywords

• digital well-being, user feedback, natural language processing, clustering