Quality-related concerns are often coined with the terms non-functional requirements, architecturally significant requirements, and quality attributes. Collectively, these qualities affect non-behavioral concerns of the software system such as reliability, usability, security, or maintainability among others. As a byproduct of a long-term maintenance effort, these system qualities tend to erode over time, causing system-wide failures that emerge via \emph{quality-related bugs}. Quality-related bugs can have a detrimental impact on system's sustained stability and can chiefly hinder its core functionality. Typically, for the developers, to manually examine these high-impacted quality-related bugs can become prohibitively expensive and impractical task to attain. This is often a case with bugs that are reported from medium or large-sized projects such as Eclipse. To alleviate this problem, we built a quality-based classifier to automatically detect these emerging quality-related concerns from textual descriptions of bug report summaries. Specifically, we leveraged a weighted combination of semantics, lexical, and shallow features in conjunction with the Random Forest ensemble learning method. Finally, we discuss the practical applicability of our classifier for mapping and visualizing quality-related concerns into the codebase with an example from the Derby project. To summarize, this work represents an effort and an early awareness to improve the underlying management of issue tracking systems and stakeholder requirements in open-source communities.