Fakespot alternative: AI-powered review analysis for Amazon. Spot suspicious reviews and fake review campaigns.
查看最近 30 天的用户变化。
按时间查看评分波动，辅助判断近期口碑是否稳定。
同口径展示 1 天、7 天、30 天的绝对增长与增长率。
查看发布时间、版本、支持语言、最近更新和抓取时间。
查看插件说明、主要功能和适用场景。
Spot fake Amazon reviews before you buy.
Review Radar for Amazon uses AI to analyse Amazon product reviews and show you which ones to trust. Unlike tools that give you a single letter grade for the whole product, we show you a trust indicator on every single review — so you can make your own informed decision.
Adjusted Star Rating
As reviews are analysed, Review Radar for Amazon calculates an adjusted star rating for the product — what the rating would look like with fake reviews filtered out. Reviews from trusted sources keep their original stars, while low-trust reviews have their ratings inverted (a suspicious 5-star review counts as 1 star and a suspicious 1 star review counts as 5 stars). Uncertain reviews are excluded entirely. Recent reviews carry more weight than older ones, so the rating reflects the current state of the product. The adjusted rating appears once 8 or more reviews for a given product have been analysed across all Review Radar for Amazon users.
How it works
• Visit any Amazon product page
查看最近评论和评分分布。
查看 Chrome 商店详情页中的相关产品。
Automate design uploads across Amazon Merch, TeePublic, and Redbubble. Includes Amazon BSR & ASIN scraper for market research.
AI-Powered Amazon Product Research Tool: Discover profitable products, estimate sales, and leverage AI insights for smarter selling.
Real Amazon sales data, not BSR estimates. Sales metrics, keyword revenue, backend spy & market analysis on product & search pages.
Shopping Assistant: Save Time by AI. Discover Price History, Avoid Costly Deals, Spot Fake Reviews, Spot Seller's Negative Rating.
Find a profitable product and start selling on Amazon quickly! Amazon product research made easy! See sales estimates and FBA fees.
AI-powered fake review detector. 1) Go to any Amazon product 2) Click extension icon 3) Extract reviews 4) Get authenticity analysis
