A Dataset of Low-Rated Applications from the Amazon Appstore for User Feedback Analysis
Nek Dil Khan, Javed Ali Khan, Darvesh Khan, Jianqiang Li, Mumrez Khan, Shah Fahad Khan

TL;DR
This paper presents a new dataset of user reviews from low-rated Amazon Appstore applications, annotated for common issues, to facilitate machine learning research and improve software quality based on user feedback.
Contribution
Introduces a curated dataset of 79,821 reviews from low-rated apps, with a manually annotated subset for classifying user feedback into six key issue categories.
Findings
Dataset enables automated classification of user feedback.
Annotated reviews reveal prevalent issues in low-rated apps.
Publicly available data supports research on app improvement strategies.
Abstract
In todays digital landscape, end-user feedback plays a crucial role in the evolution of software applications, particularly in addressing issues that hinder user experience. While much research has focused on high-rated applications, low-rated applications often remain unexplored, despite their potential to reveal valuable insights. This study introduces a novel dataset curated from 64 low-rated applications sourced from the Amazon Software Appstore (ASA), containing 79,821 user reviews. The dataset is designed to capture the most frequent issues identified by users, which are critical for improving software quality. To further enhance the dataset utility, a subset of 6000 reviews was manually annotated to classify them into six district issue categories: user interface (UI) and user experience (UX), functionality and features, compatibility and device specificity, performance and…
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Taxonomy
TopicsSoftware Engineering Research · Advanced Malware Detection Techniques · Software Testing and Debugging Techniques
