Backlink-Quality-Scorer: Backlink Quality and Risk Scoring

Type: Commercial | Domain: SEO, Link Building
Hugging Face: syeedalireza/backlink-quality-scorer

Score backlinks by quality and spam/risk for link audits and disavow decisions.

Author

Alireza Aminzadeh

• Hugging Face: syeedalireza
• LinkedIn: alirezaaminzadeh
• Email: alireza.aminzadeh@hotmail.com

Problem

Not all backlinks are equal. Automating quality and risk signals helps prioritize manual review and disavow lists.

Approach

• Input: URL, domain_authority (or similar), anchor_text, link_type (dofollow/nofollow), ref_domain_count, etc.
• Output: Quality score (0–1) and/or risk score (spam likelihood); optional binary keep/disavow.
• Models: XGBoost/LightGBM on tabular features; optional text embedding for anchor or URL for spam detection.

Tech Stack

Category	Tools
ML	scikit-learn, XGBoost, LightGBM
Data	pandas, NumPy
Optional NLP	sentence-transformers (anchor/URL)

Setup

pip install -r requirements.txt

Usage

python train.py
python inference.py --input data/backlinks.csv --output scored_links.csv

Project structure

10_backlink-quality-scorer/
├── config.py
├── train.py           # Quality (regression) and/or risk (classification)
├── inference.py       # Add pred_quality_score, pred_risk_label
├── requirements.txt
├── .env.example
├── data/
│   └── backlinks.csv      # Sample: features + quality_score, risk_label
└── models/

Data

• Sample data (included): data/backlinks.csv — columns: url, domain_authority, dofollow, ref_domains, anchor_text (optional; anchor_length is derived), same_topic; targets: quality_score (0–1), risk_label (0/1).
• Set DATA_PATH in .env if using another file.

License

MIT.