RECLASS: Multi-Task Deep Learning for App Review Classification
COMP6013 | Oxford Brookes University | 2025-26
Project Overview
RECLASS is a multi-task learning system which uses a shared BERT encoder with task-specific classification heads.
| Task | Output | Classes |
|---|---|---|
| Bug Report Detection | Binary | Yes / No |
| Feature Request Detection | Binary | Yes / No |
| Aspect Classification | Multi-class | Driver, App, Pricing, Service, Payment, General |
| Aspect Sentiment | Multi-class | Positive, Neutral, Negative |
Dataset
- Source: Uber Customer Reviews (Kaggle)
- Original size: 1,069,616 reviews
- Cleaned size: 495,036 reviews (after removing short/duplicate reviews)
- Annotation target: 5,000 manually labelled reviews
Repository Structure
## Repository Structure
6013/ README.md .gitignore data/ uber_reviews.csv # Raw dataset uber_reviews_cleaned.csv # Preprocessed reviews uber_reviews_sampled.csv # Stratified sample for annotation uber_reviews_tagged.csv # Annotated reviews (in progress) notebooks/ preprocessing_uber.ipynb # Preprocessing analysis uber_cleaned.ipynb # Cleaned data verification src/ preprocess.py # Text cleaning and filtering pipeline sampler.py # Stratified sampling strategies multitag.py # GUI annotation tool train.py # Model training (in progress) infer.py # Inference pipeline (in progress) outputs/ figures/
## Current Progress
- Manual annotation of 5,000 reviews
- BERT baseline implementation
- Multi-task model architecture
- Training and evaluation
- Comparative analysis (MTL vs single-task)
- Final report and presentation
## Installation
Clone repository
...
Create conda environment
...
Install dependencies
...requirements.txt
## Usage
## References
## Licenses
---
*Last updated: January 2025*