  Mobile app review analysis presents unique challenges due to the low quality,
subjective bias, and noisy content of user-generated documents. Extracting
features from these reviews is essential for tasks such as feature
prioritization and sentiment analysis, but it remains a challenging task.
Meanwhile, encoder-only models based on the Transformer architecture have shown
promising results for classification and information extraction tasks for
multiple software engineering processes. This study explores the hypothesis
that encoder-only large language models can enhance feature extraction from
mobile app reviews. By leveraging crowdsourced annotations from an industrial
context, we redefine feature extraction as a supervised token classification
task. Our approach includes extending the pre-training of these models with a
large corpus of user reviews to improve contextual understanding and employing
instance selection techniques to optimize model fine-tuning. Empirical
evaluations demonstrate that this method improves the precision and recall of
extracted features and enhances performance efficiency. Key contributions
include a novel approach to feature extraction, annotated datasets, extended
pre-trained models, and an instance selection mechanism for cost-effective
fine-tuning. This research provides practical methods and empirical evidence in
applying large language models to natural language processing tasks within
mobile app reviews, offering improved performance in feature extraction.
