Product Review Scraping & Sentiment Analysis.
Extract millions of customer reviews, star ratings, verified buyer badges, customer photos, and product feedback from Amazon, Walmart, Target, Google Shopping, and DTC brand stores to fuel Voice of Customer (VoC) analytics.
100%
Verified Buyer Capture
Aspect NLP
Feature Flaw Extraction
Media
User Photos & Videos
{
"platform": "Amazon US",
"asin": "B08N5M7S6K",
"review_id": "R3A9K1L49182",
"author": "Michael R.",
"rating": 5,
"review_title": "Incredible sound quality and battery life",
"review_text": "Noise cancelling is best in class. Battery lasted through an entire 14-hour flight.",
"verified_purchase": true,
"helpful_votes": 84,
"nlp_sentiment_score": 0.96,
"aspect_mentions": {
"noise_cancelling": "positive (+0.98)",
"battery_life": "positive (+0.95)",
"comfort_fit": "positive (+0.89)"
},
"review_date": "2026-08-20"
}Unlocking the Voice of the Customer Across Global Retail Ecosystems.
Customer reviews contain the most actionable intelligence in e-commerce: why shoppers love a product, the exact defects causing high return rates, and the specific phrases customers use when searching for alternatives.
However, extracting review corpora at scale presents severe technical bottlenecks. E-commerce platforms like Amazon, Walmart, and Target actively protect review listings behind strict rate-limits, anti-scraping CAPTCHAs, and dynamic AJAX pagination routines.
Our review scraping and sentiment analysis pipelines circumvent these hurdles. We extract raw unedited reviews, filter verified purchases from promotional Vine voices, download customer-uploaded photos, and run advanced Aspect-Based Sentiment Analysis (ABSA) to map customer sentiment directly to specific product features.
Strategic Customer Review Intelligence Applications
Product Flaw Identification & Return Reduction
Discover recurring manufacturing defects, zipper failures, or sizing inconsistencies before they escalate into high return rates and negative feedback spirals.
Competitor Vulnerability Exploitation
Mine 1-star and 2-star reviews on leading competitor products to identify feature weaknesses and guide your next product iteration or advertising angles.
Counterfeit & Quality Degradation Detection
Receive automated alerts when sudden spikes in negative reviews report fake packaging, missing parts, or compromised ingredients from unauthorized third-party sellers.
LLM Training Datasets & Consumer Insights
Train internal generative AI models and consumer research engines on millions of authentic, domain-specific consumer satisfaction narratives.
Customer Sentiment Feeds
Built Specifically for Consumer Brands & Market Researchers.
We extract 100% of authentic consumer feedback across major retail channels and enrich it with machine learning sentiment metrics.
Verified Purchase Filtering
Extract and flag "Verified Purchase" badges, distinguishing genuine buyers from incentivized reviewers, Amazon Vine Program testers, and promotional submissions.
Aspect-Based Sentiment (ABSA)
Segment reviews by product dimension (e.g. Battery Life, Build Quality, Ease of Assembly, Customer Service) and score sentiment polarity per attribute.
Customer Photos & Videos
Scrape full-resolution URLs of user-submitted photographs and unboxing videos to inspect real-world product appearance, packaging condition, and wear.
Helpful Votes & Influence
Record upvote counts ("45 people found this helpful") to quantify which specific reviews exercise the highest influence over consumer purchasing decisions.
Multi-Marketplace & Languages
Extract customer reviews across international marketplaces (Amazon US, UK, DE, JP, FR) with automated translation and normalized star ratings.
Negative Review Alerts
Configure instant Slack, email, or webhook notifications whenever a new 1-star or 2-star review is detected across your brand catalog.
Standard Review & Sentiment Schema Attributes.
Below are the primary normalized fields delivered in our customer review data feeds:
| Field Name | Data Type | Description | Example Value |
|---|---|---|---|
| review_id | String | Unique platform identifier assigned to the review submission. | "R3A9K1L49182" |
| product_id | String | Marketplace catalog identifier (e.g. ASIN or Walmart Item ID). | "B08N5M7S6K" |
| author_name | String | Public display name or handle of the reviewer. | "Michael R." |
| star_rating | Integer / Float | Customer rating score (standardized on a 1.0 to 5.0 scale). | 5.0 |
| review_title | String | Header or title line written by the customer. | "Incredible sound quality and battery life" |
| review_body | String | Full unedited verbatim text of the review. | "Noise cancelling is best in class..." |
| verified_purchase | Boolean | True if the platform confirmed an authentic purchase transaction. | true |
| helpful_vote_count | Integer | Number of other shoppers who marked this review helpful. | 84 |
| sentiment_score | Float | Machine learning sentiment score ranging from -1.0 (negative) to +1.0 (positive). | +0.96 |
| user_media_urls | Array | Direct image and video CDN URLs attached to the review. | ["https://images.../rev_img1.jpg"] |
Frequently Asked Questions: Review Scraping.
Insights on pagination handling, NLP sentiment categorization, and media extraction.
Tap into Authentic Customer Voice Feeds.
Request a custom review extraction sample with verified buyer badges, customer media, and Aspect-Based NLP sentiment scoring.