An overwhelming number of users access app repositories like App Store/Google Play and social media platforms like Twitter, where they provide feedback on digital experiences. This vast textual corpus comprising user feedback has the potential to unearth detailed insights regarding the users’ opinions on products and services. Various tools have been proposed that employ natural language processing (NLP) and traditional machine learning (ML) based models as an inexpensive mechanism to identify requirements in user feedback. However, they fall short on their classification accuracy over unseen data due to factors like the cost of generating voluminous de-biased labeled datasets and general inefficiency. Recently, Van Vliet et al. achieved state-of-the-art results extracting and classifying requirements from user reviews through traditional crowdsourcing. Based on their reference classification tasks and outcomes, we successfully developed and validated a deep-learning-backed artificial intelligence pipeline to achieve a state-of-the-art averaged classification accuracy of ~87% on standard tasks for user feedback analysis. This approach, which comprises a BERT-based sequence classifier, proved effective even in extremely low-volume dataset environments. Additionally, our approach drastically reduces the time and costs of evaluation, and improves on the accuracy measures achieved using traditional ML-/NLP-based techniques.