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

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Create conda environment

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Install dependencies

...requirements.txt


## Usage
## References
## Licenses

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*Last updated: January 2025*

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