# Zero-shot Bilingual App Reviews Mining with Large Language Models

arXiv: https://arxiv.org/abs/2311.03058 · HTML: https://mlsanity.com/paper/2311.03058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning](https://mlsanity.com/paper/1909.05504/) (2019)
- [Where is Your App Frustrating Users?](https://mlsanity.com/paper/2204.09310/) (2022)
- [Unsupervised Topic Discovery in User Comments](https://mlsanity.com/paper/2108.08543/) (2021)
- [Listening to Users' Voice: Automatic Summarization of Helpful App Reviews](https://mlsanity.com/paper/2210.06235/) (2022)
- [Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews](https://mlsanity.com/paper/2108.00663/) (2021)
- [Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews](https://mlsanity.com/paper/2206.14669/) (2022)

## Built on by (most cited first, 3 of 3)

- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [Automatic Classification of User Requirements from Online Feedback -- A Replication Study](https://mlsanity.com/paper/2507.21532/) (2025)
- [From Reviews to Requirements: Can LLMs Generate Human-Like User Stories?](https://mlsanity.com/paper/2603.28163/) (2026)

## Abstract (arXiv)

App reviews from app stores are crucial for improving software requirements. A large number of valuable reviews are continually being posted, describing software problems and expected features. Effectively utilizing user reviews necessitates the extraction of relevant information, as well as their subsequent summarization. Due to the substantial volume of user reviews, manual analysis is arduous. Various approaches based on natural language processing (NLP) have been proposed for automatic user review mining. However, the majority of them requires a manually crafted dataset to train their models, which limits their usage in real-world scenarios. In this work, we propose Mini-BAR, a tool that integrates large language models (LLMs) to perform zero-shot mining of user reviews in both English and French. Specifically, Mini-BAR is designed to (i) classify the user reviews, (ii) cluster similar reviews together, (iii) generate an abstractive summary for each cluster and (iv) rank the user review clusters. To evaluate the performance of Mini-BAR, we created a dataset containing 6,000 English and 6,000 French annotated user reviews and conducted extensive experiments. Preliminary results demonstrate the effectiveness and efficiency of Mini-BAR in requirement engineering by analyzing bilingual app reviews. (Replication package containing the code, dataset, and experiment setups on https://github.com/Jl-wei/mini-bar )