UPP (UL Certificated) 36V/48V/52V Hailong/Shark Ebike Battery for 500W 750W 1000W 1500W Bafang &Other Ebike Conversion Wheel Kit(with Smart BMS)

UPP (UL Certificated) 36V/48V/52V Hailong/Shark Ebike Battery for 500W 750W 1000W 1500W Bafang &Other Ebike Conversion Wheel Kit(with Smart BMS)

ASIN: B0CQ7W1W2N
Analysis Date: Oct 15, 2025 (re-analyzed Oct 15, 2025)

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Review Analysis Results

C
Authenticity Grade
28.00%
Fake Reviews
4.55
Original Rating
3.90
Adjusted Rating

Analysis Summary

The reviews show mixed authenticity signals. On the positive side, there's reasonable diversity in ratings (5, 4, and 3 stars), some reviews mention specific technical details like '25ah with grade-a cells' and '52v 24ah', and multiple reviewers note the missing USB port mentioned in the product description, suggesting genuine usage experience. However, concerning patterns include: review R3835NXRUOGZQY appears duplicated with identical content, several reviews are extremely brief and generic ('Powerful and long last ing', 'Performs well'), and there's a suspiciously high concentration of 5-star reviews (7 out of 10) with enthusiastic but vague language. The moderate fake percentage reflects that while some reviews appear authentic, others show characteristics of incentivized or low-effort reviews.

Review Statistics

64
Total Reviews on Amazon
-0.65
Rating Difference

Price Analysis

Price analysis pending

Price insights will be available shortly.

Understanding This Analysis

What does Grade C mean?

This product has moderate review authenticity concerns. A notable portion of reviews show suspicious patterns. Consider reading reviews carefully before purchasing.

Adjusted Rating Explained

The adjusted rating (3.90 stars) represents what we estimate this product's rating would be if fake reviews were removed. This product's adjusted rating is lower than Amazon's displayed rating (4.55 stars), suggesting positive fake reviews may be inflating the score.

How We Detect Fake Reviews

Our AI analyzes multiple factors: language patterns (generic vs. specific), reviewer behavior (history, timing), temporal anomalies (review clusters), verification status, sentiment authenticity, and statistical outliers. No single factor determines a review is fake - we look at the combination of signals.

Important Limitations

No automated system is perfect. Sophisticated fake reviews can evade detection, and some genuine reviews may be incorrectly flagged. Use this analysis as one data point in your purchasing decision, not the only factor. Reading actual review content yourself is always valuable.

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