Leveraging Large Language Models for Mobile App Review Feature Extraction

• URL: http://arxiv.org/abs/2408.01063v1
• Date: Fri, 2 Aug 2024 07:31:57 GMT
• Title: Leveraging Large Language Models for Mobile App Review Feature Extraction
• Authors: Quim Motger, Alessio Miaschi, Felice Dell'Orletta, Xavier Franch, Jordi Marco,
• Abstract summary: 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. Empirical evaluations demonstrate that this method improves the precision and recall of extracted features and enhances performance efficiency.
• Score: 4.879919005707447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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