# Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews

arXiv: https://arxiv.org/abs/2206.14669 · HTML: https://mlsanity.com/paper/2206.14669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning](https://mlsanity.com/paper/1909.05504/) (2019)
- [Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews](https://mlsanity.com/paper/2108.00663/) (2021)

## Built on by (most cited first, 2 of 2)

- [Zero-shot Bilingual App Reviews Mining with Large Language Models](https://mlsanity.com/paper/2311.03058/) (2023)
- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)

## 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.