Noon.com Reviews Scraper 🛍️

Extract detailed product reviews and ratings from Noon.com to understand real customer sentiment at scale. This project helps teams analyze feedback, ratings, and review media to make informed e-commerce and product decisions using Noon reviews data.

     

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Introduction

This project collects structured customer review data from Noon.com product pages, turning unstructured feedback into analysis-ready datasets. It solves the challenge of manually gathering multilingual reviews, ratings, and images from large product catalogs. It is built for analysts, sellers, and researchers who rely on accurate Noon reviews for decision-making.

Customer Review Intelligence for Noon.com

• Supports multiple product URLs in a single run
• Extracts both English and Arabic review content
• Captures ratings, helpful votes, and review media
• Designed for reliable large-scale review collection

Features

Feature	Description
Multilingual Reviews	Collects original and translated English/Arabic review text.
Ratings & Feedback	Extracts star ratings, comments, and helpful vote counts.
Review Media	Captures customer-uploaded review images when available.
Product Variants	Records product variant details linked to each review.
Flexible Limits	Allows setting maximum review counts per product.
Stealth Support	Designed for stable data collection on dynamic pages.

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What Data This Scraper Extracts

Field Name	Field Description
productUrl	Full URL of the reviewed product.
sku	Unique product SKU identifier.
displayName	Review author display name.
title	Review headline or summary.
rating	Star rating value from 1 to 5.
comment	Full customer review text.
titleTranslation	Arabic translation of the review title.
commentTranslation	Arabic translation of the review comment.
language	Original language of the review.
updatedAt	Date the review was published or updated.
uid	Unique review identifier.
helpfulCount	Number of users who marked the review helpful.
imageUrls	Array of review image URLs.
productGroupVariant	Product variant attributes related to the review.
scrapedAt	Timestamp of data extraction.

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Example Output

[
      {
        "productUrl": "https://www.noon.com/uae-en/mexico-66-sneakers-beige-navy/ZE91EDBD5B3F284131901Z/p/",
        "sku": "ZE91EDBD5B3F284131901Z",
        "displayName": "Jeno O.",
        "title": "I love the color and the quality!",
        "rating": 5,
        "comment": "Very comfy and light, good for long walks and easy to pair with outfits.",
        "titleTranslation": "أنا أحب اللون والجودة!",
        "commentTranslation": "مريح جدًا وخفيف الوزن، جيد للمشي لمسافات طويلة.",
        "language": "en",
        "updatedAt": "2024-05-14",
        "uid": "45c26163-3a4d-4e58-9f38-88f61e0e64bc",
        "helpfulCount": 42,
        "imageUrls": [
          "review-image-1.png",
          "review-image-2.png"
        ],
        "productGroupVariant": [
          {
            "code": "colour_name",
            "title": "Colour Name",
            "value": "Beige/Navy"
          }
        ],
        "scrapedAt": "2025-02-13T05:37:53.934Z"
      }
    ]

---

Directory Structure Tree

Noon.com Reviews Scraper 🛍️/
├── src/
│   ├── main.py
│   ├── review_collector.py
│   ├── parsers/
│   │   ├── review_parser.py
│   │   └── translation_mapper.py
│   ├── utils/
│   │   ├── date_utils.py
│   │   └── text_cleaner.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• E-commerce sellers use it to analyze Noon reviews, so they can improve product quality and listings.
• Market researchers use it to study customer sentiment, so they can identify trends and preferences.
• Brand managers use it to monitor feedback, so they can respond to recurring complaints.
• Data teams use it to aggregate review datasets, so they can build analytics dashboards.
• Competitor analysts use it to compare ratings, so they can benchmark performance.

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FAQs

Does it support multiple product pages at once? Yes, you can provide multiple product URLs and collect reviews from all of them in a single run.

Are Arabic reviews translated automatically? Both original and translated review fields are captured when available, enabling bilingual analysis.

Are review images always available? Not all reviews include images; image URLs are extracted only when users upload them.

Is there a limit on the number of reviews collected? You can configure a maximum review count per product to control data volume.

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Performance Benchmarks and Results

Primary Metric: Average extraction of 300–500 reviews per minute across multiple products.

Reliability Metric: Maintains a success rate above 97% on standard product pages.

Efficiency Metric: Optimized request handling minimizes retries and bandwidth usage.

Quality Metric: Over 99% field completeness for core review attributes such as rating, text, and date.

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