TL;DR
This paper presents an automated method using CamemBERT to analyze French user reviews for requirements engineering, enabling the automatic identification of feature requests from a dataset of 6000 reviews.
Contribution
The study introduces a novel multi-label classification dataset and demonstrates the effectiveness of CamemBERT in automatically detecting user requests in reviews.
Findings
Encouraging classification accuracy for feature request detection
Feasibility of automated analysis of user reviews in requirements engineering
Creation of a publicly available French review dataset
Abstract
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.
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