Integrating Multi-Label Classification and Generative AI for Scalable Analysis of User Feedback
arXiv:2601.23018v1 Announce Type: cross Abstract: In highly competitive software markets, user experience (UX) evaluation is crucial for ensuring software quality and fostering long-term product success. Such UX evaluations typically combine quantitative metrics from standardized questionnaires with qualitative feedback collected through open-ended questions. While open-ended feedback offers valuable insights for improvement and helps explain quantitative results, analyzing large volumes of user comments is challenging and time-consuming. In this paper, we present techniques developed during a
The proliferation of software and digital services has led to an explosion of user feedback, making manual analysis increasingly unsustainable and inefficient.
This development allows companies to scale user feedback analysis, leading to more responsive product development, improved user experience, and potentially higher market competitiveness.
Traditional, labor-intensive methods of qualitative user feedback analysis are being augmented or replaced by automated, AI-driven approaches, enabling faster iteration and deeper insights.
- · Software companies
- · UX researchers
- · AI software providers
- · Product development teams
- · Manual data analysis services
- · Companies slow to adopt AI tools
Automated feedback analysis tools become standard in software development workflows.
Faster product iteration cycles lead to more dynamic and competitive software markets.
The quality and responsiveness of software products significantly improve across industries, impacting user loyalty and market share.
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