# Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning

arXiv: https://arxiv.org/abs/1909.05504 · HTML: https://mlsanity.com/paper/1909.05504/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews](https://mlsanity.com/paper/2104.05861/) (2021)
- [Efficient, Uncertainty-based Moderation of Neural Networks Text Classifiers](https://mlsanity.com/paper/2204.01334/) (2022)
- [Zero-shot Bilingual App Reviews Mining with Large Language Models](https://mlsanity.com/paper/2311.03058/) (2023)
- [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)
- [App Review Driven Collaborative Bug Finding](https://mlsanity.com/paper/2301.02818/) (2023)
- [Classification of Natural Language Processing Techniques for Requirements Engineering](https://mlsanity.com/paper/2204.04282/) (2022)
- [On the Identification of the Energy related Issues from the App Reviews](https://mlsanity.com/paper/2304.11292/) (2023)
- [OLGA: One-cLass Graph Autoencoder](https://mlsanity.com/paper/2406.09131/) (2024)
- [Automatic Classification of User Requirements from Online Feedback -- A Replication Study](https://mlsanity.com/paper/2507.21532/) (2025)

## Abstract (arXiv)

With the rise of social media like Twitter and of software distribution platforms like app stores, users got various ways to express their opinion about software products. Popular software vendors get user feedback thousandfold per day. Research has shown that such feedback contains valuable information for software development teams such as problem reports or feature and support inquires. Since the manual analysis of user feedback is cumbersome and hard to manage many researchers and tool vendors suggested to use automated analyses based on traditional supervised machine learning approaches. In this work, we compare the results of traditional machine learning and deep learning in classifying user feedback in English and Italian into problem reports, inquiries, and irrelevant. Our results show that using traditional machine learning, we can still achieve comparable results to deep learning, although we collected thousands of labels.