Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
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Abstract (arXiv)
We are concerned by Data Driven Requirements Engineering, and in particular the consideration of user's reviews. These online reviews are a rich source of information for extracting new needs and improvement requests. In this work, we provide an automated analysis using CamemBERT, which is a state-of-the-art language model in French. We created a multi-label classification dataset of 6000 user reviews from three applications in the Health & Fitness field. The results are encouraging and suggest that it's possible to identify automatically the reviews concerning requests for new features. Dataset is available at: https://github.com/Jl-wei/APIA2022-French-user-reviews-classification-dataset.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 29 Jun 2022.
Builds on
6 of 17 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2019CamemBERT: a Tasty French Language Modelcited by 368
- 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 140
- 2019Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learningcited by 10
- 2021Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviewscited by 2
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.