  User reviews of mobile apps provide a communication channel for developers to
perceive user satisfaction. Many app features that users have problems with are
usually expressed by key phrases such as "upload pictures", which could be
buried in the review texts. The lack of fine-grained view about problematic
features could obscure the developers' understanding of where the app is
frustrating users, and postpone the improvement of the apps. Existing
pattern-based approaches to extract target phrases suffer from low accuracy due
to insufficient semantic understanding of the reviews, thus can only summarize
the high-level topics/aspects of the reviews. This paper proposes a
semantic-aware, fine-grained app review analysis approach (SIRA) to extract,
cluster, and visualize the problematic features of apps. The main component of
SIRA is a novel BERT+Attr-CRF model for fine-grained problematic feature
extraction, which combines textual descriptions and review attributes to better
model the semantics of reviews and boost the performance of the traditional
BERT-CRF model. SIRA also clusters the extracted phrases based on their
semantic relations and presents a visualization of the summaries. Our
evaluation on 3,426 reviews from six apps confirms the effectiveness of SIRA in
problematic feature extraction and clustering. We further conduct an empirical
study with SIRA on 318,534 reviews of 18 popular apps to explore its potential
application and examine its usefulness in real-world practice.
