Wildberries Product Reviews Scraper: Extract Customer Feedback Data

Understanding Wildberries Reviews and Their Business Value

Wildberries.ru dominates Russian e-commerce with millions of product listings and customer reviews. Product review pages contain rich feedback data: detailed text reviews, structured pros/cons, matching assessments (size, photo, description accuracy), multimedia content (photos, videos), and community engagement (votes, helpfulness ratings).

This data reveals product quality perception, common complaints, feature preferences, and sizing accuracy—critical for sellers optimizing listings, brands monitoring reputation, and researchers analyzing consumer sentiment in Russian markets. Manual collection across hundreds of products is impractical; this scraper automates extraction into structured datasets.

What This Scraper Extracts

The scraper processes product review page URLs (format: wildberries.ru/catalog/[PRODUCT_ID]/feedbacks) and extracts comprehensive review data:

Review Content: Full text reviews, structured pros/cons, product matching assessments (size, photo, description accuracy), color and size purchased.

User Data: Reviewer IDs (global and Wildberries-specific), user profile details for credibility assessment.

Ratings & Metrics: Product valuation (star rating), votes (helpful/unhelpful), helpfulness score, rank within reviews.

Media: Attached photos and videos showing actual product usage and quality.

Metadata: Review ID, creation/update dates, answer status (seller responses), parent feedback ID (reply threads), exclusion flags, classification tags.

Moderation Data: Status ID, rejection reasons, helping identify filtered or disputed reviews.

Target Users: E-commerce sellers optimize listings using size accuracy data and common complaints. Brands monitor reputation and respond to feedback. Market researchers analyze sentiment trends across products/categories. Competitive intelligence teams benchmark competitor products. Product managers identify improvement priorities from structured pros/cons.

Input Configuration

Example:

{
  "urls": ["https://www.wildberries.ru/catalog/387458057/feedbacks"],
  "ignore_url_failures": true,
  "max_items_per_url": 50
}

urls: Array of product review page URLs. Each URL must follow format https://www.wildberries.ru/catalog/[PRODUCT_ID]/feedbacks. Collect product IDs from search results or category browsing, then append /feedbacks. Can process multiple products in single run.

ignore_url_failures: Set true for batch processing—failed URLs won't stop scraping. Essential when processing 50+ products where some may be removed or restricted. Set false only if every URL must succeed.

max_items_per_url: Maximum reviews per product (default: 20). Wildberries displays reviews paginated; setting higher values (50-100) captures more feedback per product. Balance data volume against runtime—100 reviews per product across 50 products requires significant processing.

Output Structure and Field Definitions

Sample Output:

{
  "id": "uJQqkOlXegawnAJ1Q9rA",
  "global_user_id": "ykLMDLA9Tr6kHvNVE8E0+/wIE9bRVj0k4xL0St65Ffbj",
  "wb_user_id": 0,
  "wb_user_details": {
    "country": "ru",
    "name": "Людмила",
    "has_photo": false
  },
  "nm_id": 387453515,
  "text": "",
  "pros": "Телефон очень хороший  спасибо",
  "cons": "",
  "matching_size": "",
  "matching_photo": "",
  "matching_description": "",
  "product_valuation": 5,
  "color": "Синий · 64 ГБ",
  "size": "0",
  "created_date": "2026-05-05T14:22:53Z",
  "updated_date": "2026-05-05T16:50:36.311937020Z",
  "answer": {
    "text": "Благодарим за отзыв — это помогает становиться лучше!",
    "state": "wbRu",
    "last_update": null,
    "create_date": "2026-05-05T15:20:05Z",
    "reject_reason": null,
    "metadata": {
      "edit_text": "",
      "edit_reject_reason": 0
    }
  },
  "feedback_helpfulness": null,
  "video": null,
  "votes": {
    "pluses": 0,
    "minuses": 0
  },
  "rank": 1034.0,
  "status_id": 16,
  "reasons": {
    "good": [],
    "bad": []
  },
  "parent_feedback_id": null,
  "excluded_from_rating": {
    "is_excluded": true,
    "reasons": [
      "hasIncludedChild"
    ]
  },
  "tags": null,
  "from_url": "https://www.wildberries.ru/catalog/387458057/feedbacks?imtId=378736899&size=562024885"
}

ID: Unique review identifier within Wildberries system. Use: Tracking specific reviews, avoiding duplicates, referencing in databases.

Global User ID / WB User ID: Reviewer identifiers across Wildberries platform. Use: Analyzing reviewer behavior patterns, identifying power reviewers, detecting fake reviews.

WB User Details: Profile information including name, verification status, review count. Use: Assessing reviewer credibility, weighting reviews by user reputation.

NM ID: Product nomenclature ID (internal Wildberries product identifier). Use: Linking reviews to products, cross-referencing with product catalogs.

Text: Full review content in Russian. Use: Sentiment analysis, keyword extraction, natural language processing for themes.

Pros / Cons: Structured positive and negative aspects. Use: Quick identification of strengths/weaknesses, feature prioritization, comparison matrices.

Matching Size / Matching Photo / Matching Description: Boolean or rating fields indicating accuracy of size chart, product photos, and description. Use: Critical for clothing/footwear sellers—identifies if items run large/small, photos misrepresent products.

Product Valuation: Star rating (typically 1-5). Use: Quantitative quality metric, filtering reviews by satisfaction level.

Color / Size: Specific variant purchased. Use: Identifying quality variations across SKUs, size-specific issues (e.g., "Large fits like Medium").

Created Date / Updated Date: Review timestamps. Use: Tracking feedback velocity, seasonal patterns, identifying recent vs. old reviews.

Answer: Seller/brand response to review. Use: Monitoring response rates, analyzing resolution strategies, competitive benchmarking of customer service.

Feedback Helpfulness: Community rating of review usefulness. Use: Filtering most valuable reviews, identifying trusted reviewers.

Video: Attached video content URLs. Use: Visual quality assessment, unboxing experiences, usage demonstrations.

Votes: Upvotes/downvotes from community. Use: Identifying most impactful reviews, detecting manipulation (abnormal vote patterns).

Rank: Review position in sorting algorithm. Use: Understanding Wildberries' review prioritization logic.

Status ID / Reasons: Moderation status and rejection reasons. Use: Identifying censored reviews, understanding platform policies, detecting suspicious patterns.

Parent Feedback ID: Links replies to original reviews. Use: Threading conversations, analyzing seller engagement depth.

Excluded From Rating: Flag if review doesn't count toward product rating. Use: Identifying filtered or disputed feedback affecting aggregate scores.

Tags: Classification labels (verified purchase, promotional, etc.). Use: Segmenting review types, filtering authentic feedback.

Implementation Guide

1. Collect Product URLs: Browse Wildberries, identify target products, copy product IDs, construct review URLs: wildberries.ru/catalog/[ID]/feedbacks
2. Configure Input: Add URLs to array, set max_items_per_url based on needs (20 for quick scan, 100+ for deep analysis)
3. Run Scraper: Monitor progress—50 products with 50 reviews each typically completes in 5-10 minutes
4. Export Data: JSON for databases, CSV for spreadsheet analysis
5. Handle Russian Text: Ensure UTF-8 encoding when exporting to preserve Cyrillic characters

Strategic Applications

Size Chart Optimization: Aggregate matching_size scores across reviews to identify if products run large/small. Update size charts reducing returns.

Photo Quality Improvement: Low matching_photo scores indicate misleading images. Prioritize photography updates for these products.

Sentiment Trends: Track review sentiment over time—declining scores signal quality issues or increased competition.

Competitive Benchmarking: Compare your products' pros/cons against competitors' reviews for same category.

Fake Review Detection: Analyze vote patterns, user profiles, and timing—clusters of 5-star reviews from new accounts with generic text indicate manipulation.

Customer Service ROI: Measure answer response rates and subsequent review updates to quantify engagement impact.

Best Practices

Schedule Regular Scraping: Weekly scraping tracks review accumulation, identifies emerging issues early.

Cyrillic Text Processing: Use proper NLP tools for Russian language—standard English sentiment analyzers fail. Consider Yandex ML services.

Focus on Matching Scores: For fashion/apparel, matching_size is more actionable than star ratings—directly impacts return rates.

Filter Verified Purchases: Use tags to prioritize verified buyer reviews over promotional or incentivized feedback.

Monitor Excluded Reviews: High excluded_from_rating counts may indicate seller gaming ratings—competitive intelligence opportunity.

Conclusion

The Wildberries Product Reviews Scraper transforms Russia's largest e-commerce review database into actionable intelligence. From size accuracy optimization to sentiment analysis, structured review data drives product improvements, competitive positioning, and customer satisfaction in Russian markets.