RECLASS: Multi-Task Deep Learning for App Review Classification

COMP6013 | Oxford Brookes University | 2025-26

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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

6013/
README.md
requirements.txt
    multitag/
        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/
            datasets_reviews.ipynb     # Initial data exploration
            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)

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

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Last updated: January 2025