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