README.md

metadata

title: AspectBERT
emoji: 🔋
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.0
app_file: app.py
pinned: false
license: mit
tags:
  - aspect-based-sentiment-analysis
  - distilbert
  - text-classification
  - amazon-reviews
  - absa

AspectBERT

AspectBERT is a fine-tuned DistilBERT model for Aspect-Based Sentiment Analysis (ABSA) on Amazon product reviews. Given a single review, it predicts the sentiment (positive / neutral / negative) for each of 8 product aspects independently.

Aspects

battery, display, camera, price, performance, design, software, customer_service

Model description

• Base model: distilbert-base-uncased
• Input format: "{review_text} aspect: {aspect_name}"
• Architecture: DistilBERT backbone (first 4 of 6 transformer layers frozen, last 2 fine-tuned) → Linear(768, 256) → GELU → Dropout(0.2) → Linear(256, 3)
• Output: 3-way softmax over negative, neutral, positive

Training data

• Source: McAuley-Lab/Amazon-Reviews-2023 (raw_review_Electronics config), ~25,000 sampled reviews
• Aspect labeling: keyword matching — each review can produce multiple rows, one per detected aspect
• Sentiment labeling: derived from the review's star rating
  • 4–5 stars → positive
  • 3 stars → neutral
  • 1–2 stars → negative
• Split: 70% train / 15% validation / 15% test

Training procedure

• Optimizer: AdamW (lr=2e-5, weight_decay=0.01)
• Scheduler: OneCycleLR, 10% warmup, cosine decay
• Epochs: 4
• Batch size: 16 (CPU) / 32 (GPU)
• Loss: Cross-entropy
• Model selection: best checkpoint by validation macro F1

Training history is logged to results/training_history.json.

Evaluation

Reported on the held-out test split (15%):

• Macro F1
• Accuracy
• Per-class F1 (negative / neutral / positive)
• Confusion matrix
• Comparison against a VADER rule-based sentiment baseline (review-level, not aspect-aware)

Results are written to results/test_metrics.json after training (see src/train.py).

How to use

Python (HuggingFace Hub)

import os
os.environ["HF_MODEL_NAME"] = "<your-username>/aspectbert"

from src.inference import load_model, predict_all_aspects

model, tokenizer, device = load_model()
results = predict_all_aspects(
    model, tokenizer, device,
    "The battery lasts forever but the camera is disappointing in low light."
)
print(results)
# {
#   "battery": {"label": "positive", "scores": {...}},
#   "camera":  {"label": "negative", "scores": {...}},
#   ...
# }

Command line

python src/inference.py "Great screen but the battery dies way too fast." --aspect battery

Streamlit app

export HF_MODEL_NAME="<your-username>/aspectbert"
streamlit run app.py

The app supports:

• Free-text review input + 4 example reviews
• Per-aspect sentiment with confidence bars
• A radar chart of positive-sentiment scores across aspects
• LIME word-importance explanations
• A toggle to compare against a VADER baseline

Project structure

AspectBERT/
├── src/
│   ├── constants.py        # shared aspects, label maps, input formatting
│   ├── data_preparation.py # download, clean, aspect labeling, splits
│   ├── model.py             # DistilBERT + classification head
│   ├── train.py             # training loop, evaluation, checkpointing
│   └── inference.py         # predict single review, all aspects, LIME
├── notebooks/
│   └── training.ipynb       # Kaggle/Colab GPU training notebook
├── app.py                    # Streamlit UI
├── requirements.txt
├── deploy_to_hf.sh           # push app to a HuggingFace Space
└── README.md

Limitations

• Aspect labels are derived from keyword matching, which is noisy and may miss implicit aspect mentions or mislabel sarcasm.
• Sentiment labels are derived from the overall review rating, not aspect-specific ratings, so an aspect's true sentiment may occasionally differ from the review's overall rating.
• Trained on Electronics category reviews; may not generalize well to other product categories.

License

MIT