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+# RECLASS: Multi-Task Deep Learning for App Review Classification
+**RECLASS** tests whether a single shared encoder with four task-specific heads
+can match four independently trained single-task models performance, while producing all
+four predictions in one forward pass instead of four.
+
+> Trained model checkpoints, datasets, full experiment logs, and extended commit history\
+> are mirrored on:\
+> [git.charlierasberry.dev/mik/ReClass](https://git.charlierasberry.dev/mik/ReClass)
+
+## The four tasks
+
+From a single review, the model predicts:
+
+| Task | Type | Labels |
+|---|---|---|
+| Bug Report | binary | Yes / No |
+| Feature Request | binary | Yes / No |
+| Aspect | 6-class | App, Driver, General, Payment, Pricing, Service |
+| Aspect Sentiment | 3-class | Positive, Neutral, Negative |
+
+Example: *"Got charged twice for one ride, please fix"* → bug: yes, feature: no,
+aspect: payment, sentiment: negative.
+
+## Architecture
+
+![Architecture](architecture.png)
+
+- **Shared encoder:** XLM-RoBERTa-base (12 layers, 768 hidden, 12 attention heads).
+  Multilingual encoder chosen over BERT-base after data exploration showed
+  substantial language switching in individual reviews.
+- **Tokenisation:** SentencePiece, `max_length=256` (chosen after measuring
+  truncation impact on minority-class reviews at shorter lengths).
+- **Shared regularisation:** dropout (p = 0.2) applied to the shared `[CLS]`
+  representation before branching into task specific heads.
+- **Task heads:** four independent linear layers projecting the 768-dim
+  representation into each task's class space\
+   (Bug: 768→2, Feature: 768→2,
+  Aspect: 768→6, Sentiment: 768→3).
+- **Loss:** unweighted sum of four inverse-frequency-weighted cross-entropy
+  losses, per task.
+- **Optimiser:** AdamW, lr 2e-5, weight decay 0.1, linear warmup (10% of steps),
+  gradient clipping at max_norm 1.0.
+- **Early stopping:** on validation macro-F1, patience of 3 epochs.
+
+> Single task baselines use the same encoder as its MTL equivalent, the only difference is the number of heads.
+
+## Headline result: MTL matches STL
+
+McNemar's exact test on paired predictions (750 test samples per task, α = 0.05):
+
+| Task | STL Macro-F1 | MTL Macro-F1 | Δ | p-value | Significant? |
+|---|---|---|---|---|---|
+| Bug Report | 0.785 | 0.784 | −0.001 | 0.699 | No |
+| Feature Request | 0.742 | 0.763 | +0.021 | 0.653 | No |
+| Aspect | 0.694 | 0.717 | +0.023 | 0.210 | No |
+| Aspect Sentiment | 0.786 | 0.758 | −0.028 | 0.728 | No |
+
+No task shows a statistically significant difference. MTL is competitive across
+the board and slightly ahead on two of four tasks, while using only one
+encoder (~278M params), a 4×
+reduction from (\~1.1B params), and producing all four predictions in a single forward pass instead
+of four.
+
+## The keyword-boosted experiment - tradeoffs and wins
+
+A second dataset was curated using keyword sampling for minority classes - bug reports and feature requests to test whether it is worthwhile.
+
+| Task | MTL Original | MTL Boosted | Δ |
+|---|---|---|---|
+| Bug Report | 0.783 | 0.905 | **+0.122** |
+| Feature Request | 0.763 | 0.816 | +0.053 |
+| Aspect | 0.717 | 0.803 | +0.086 |
+| Aspect Sentiment | 0.757 | 0.600 | **−0.157** |
+
+Three tasks improved substantially. Aspect Sentiment collapsed. Bug-report
+keyword reviews were heavily negative skewed, so oversampling them shifted the training
+distribution to 80.9% negative sentiment, in an MTL scenario this negatively affects the ability to learn the shared representations.
+
+> **In a shared-encoder MTL setup, data curation for one task is never just
+> curation for that task alone.** Shared Learning is also shared interference.
+
+*(The models were not cross-evaluated - see Limitations.)*
+
+## Dataset
+
+Manually annotated from the [Ola Vs Uber Reviews](https://www.kaggle.com/datasets/khushipitroda/ola-vs-uber-play-store-reviews) dataset
+using a custom Tkinter annotation tool (`multitag.py`) with keyboard shortcuts
+and enforced label completeness.
+
+| Stage | Count |
+|---|---|
+| Raw corpus | 1,069,616 |
+| After cleaning + ≥5-word filter | 495,036 |
+| Original distribution, annotated | 4,999 |
+| Keyword-boosted, annotated | 4,997 |
+| Split | 70% train / 15% val / 15% test |
+
+Class imbalance is severe in the wild: bug reports are 18.4% of the original
+set, neutral sentiment just 5.2%. Handled via inverse-frequency class weighting
+during training, not by rebalancing the raw data.
+
+**Data availability:** the annotated datasets (original and keyword-boosted,
+train/val/test splits) are included in this repository under `data/`. Raw
+source: [Uber Customer Reviews on Kaggle](https://www.kaggle.com/datasets/rajatraj0502/ola-vs-uber-reviews).
+
+## Repository structure
+
+```
+.
+├── architecture.png    # Overview diagram
+├── environment.yml     # conda environment spec
+├── data/               # annotated original + keyword-boosted splits
+├── notebooks/          # preprocessing, annotation QA, analysis, inference prep
+├── src/
+│   ├── preprocess.py   # cleaning + filtering pipeline
+│   ├── sampler.py      # original / keyword-boosted sampling strategies
+│   ├── multitag.py     # Tkinter annotation tool
+│   ├── dataset.py      # tokenisation + PyTorch Dataset
+│   ├── model.py        # shared-encoder MTL + single-task model definitions
+│   ├── train.py        # training loop (MTL + STL)
+│   ├── evaluate.py     # Macro-F1, confusion matrices, McNemar's test
+│   └── infer.py        # inference on new review text
+└── README.md
+```
+
+## Running the pipeline
+
+### 0. Optional Setup and get data 
+```bash
+cd <project location> # replace with location
+mkdir -p data/
+mkdir -p data/raw/
+mkdir -p data/processed/
+mkdir -p outputs/
+mkdir -p runs/ # if using tensorboard
+mkdir -p outputs/inference/
+
+# Optionally replace with own data though could cause slight issues
+curl -L -o data/raw/ola-vs-uber-play-store-reviews.zip\<newline>
+  https://www.kaggle.com/api/v1/datasets/download/khushipitroda/ola-vs-uber-play-store-reviews
+
+cd data/raw
+unzip ola-vs-uber-play-store-reviews.zip
+mv "Uber Customer Reviews.csv" uber_reviews.csv
+rm ola-vs-uber-play-store-reviews.zip
+rm “Ola Customer Reviews.csv”
+cd ../..
+
+conda env create -f environment.yml
+conda activate reclass
+
+```
+
+
+
+### 1. Preprocess raw reviews (clean, filter, sample)
+```bash
+python src/preprocess.py [optionally fill in INPUT and OUTPUT at top of file]
+```
+
+
+### 2. Sample cleaned reviews for tagging
+```bash
+python src/sampler.py [optionally fill in target_samples, INPUT and OUTPUT at top of file]
+```
+
+
+### 3. Annotate (or use existing labelled CSVs)
+```bash
+python src/multitag.py [optionally fill in INPUT and OUTPUT at top of file]
+```
+
+
+### 3.5 Train Test Split
+Use the notebook preprocessing_tagged.ipynb to create splits,
+cells 2 and 23 contain input/output paths. 
+- Inputs data/raw/uber_reviews_tagged_original.csv and /data/raw/uber_reviews_tagged_boosted.csv by default. 
+- Outputs to data/tagged_boosted_cleaned.csv and data/tagged_original_cleaned.csv by default.
+
+
+### 4. Train - MTL or single-task baseline
+```bash
+python src/train.py 
+Defaults to python src/train.py --mode mtl --task all --dataset original –batch_size 16 --epochs 5 --patience 3 --lr 2e-5
+```
+
+
+### 5. Evaluate against test set
+```bash
+python src/evaluate.py
+
+options:
+  -h, --help            show this help message and exit
+  --mode {mtl,stl}      mtl or stl
+  --task {all,bug_report,feature_request,aspect,aspect_sentiment}
+  --dataset {original,boosted}
+  --model_path MODEL_PATH .pt file path
+  --batch_size BATCH_SIZE
+
+e.g. python src/evaluate.py --mode mtl --dataset original --model_path outputs/best_model_mtl_original.pt
+```
+
+
+### 6. Run inference on new text 
+```bash
+python src/infer.py
+options:
+  -h, --help            show this help message and exit
+  --model MODEL         .pt file - just the filename not path
+  --task {all,bug_report,feature_request,aspect,aspect_sentiment}
+  --interactive         Loops reading input until exit()
+  --text                Use command line text for input
+  --dataset DATASET     Enter a file name for inference (stored in data/processed/)
+  --batch_size BATCH_SIZE
+  --mode {mtl,stl}      		mtl or stl
+  --text_column TEXT_COLUMN 		Where is the text column
+
+e.g. python src/infer.py --model best_model_mtl_original.pt --mode mtl --text  
+```
+
+
+>Trained on a single NVIDIA RTX 2070 Super (8GB VRAM), ~12 GPU-hours total across all reported runs.
+
+## Limitations
+
+- **Single annotator.** No inter-annotator agreement / Cohen's Kappa, so inconsistencies / bias can't be ruled out.
+- **No cross-evaluation.** The boosted model was only tested on its own
+  (curated - not original distribution) test set, so its performance on the real data was left unknown. 
+- **Single domain.** Cross domain performance was left unknown. 
+- **Overconfidence on errors.** Overconfidence was too high and needs calibrating in the future. 
+- **Linear classification heads.** Tasks with multiple classes would likely benefit from an additional layer.
+
+## Future work
+
+- Cross-evaluate the boosted model on the original test set, quickest next step.
+- Second annotator on any future data labelling.
+- Dynamic or learned weighting.
+- Possibly implement the transferrable elements elsewhere on future projects.
+- Temperature scaling on the overconfidence.
+
+## Citation / provenance
+
+Built as a BSc Computer Science dissertation project, Oxford Brookes University
+(2026). Uses the [Ola Vs Uber Reviews](https://www.kaggle.com/datasets/khushipitroda/ola-vs-uber-play-store-reviews) also uploaded here [Ola Vs Uber Play store reviews](https://www.kaggle.com/datasets/rajatraj0502/ola-vs-uber-reviews)
+from Kaggle, and [XLM-RoBERTa](https://arxiv.org/abs/1911.02116)
+(Conneau et al., 2020) via HuggingFace Transformers.
+
+## License
+
+*[MIT - see LICENSE](LICENSE)*