A Dataset of Low-Rated Applications from the Amazon Appstore for User Feedback Analysis

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 stability, customer support and responsiveness, and security and privacy issues. This annotated dataset is a valuable resource for developing machine learning-based approaches aiming to automate the classification of user feedback into various issue types. Making both the annotated and raw datasets publicly available provides researchers and developers with a crucial tool to understand common issues in low-rated apps and inform software improvements. The comprehensive analysis and availability of this dataset lay the groundwork for data-derived solutions to improve software quality based on user feedback. Additionally, the dataset can provide opportunities for software vendors and researchers to explore various software evolution-related activities, including frequently missing features, sarcasm, and associated emotions, which will help better understand the reasons for comparatively low app ratings.