Aidatain Green Forest Natural Tapestry Window Scenery Wall Aesthetic Tapestry for Bedroom Flannel 90" L x 84" W Beach Blanket Living Dinning Room College Dorm Wall Hanging GTDJAT0092

Aidatain Green Forest Natural Tapestry Window Scenery Wall Aesthetic Tapestry for Bedroom Flannel 90" L x 84" W Beach Blanket Living Dinning Room College Dorm Wall Hanging GTDJAT0092

ASIN: B0CNJX7LLF
Analysis Date: Sep 23, 2025 (re-analyzed Sep 23, 2025)

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Review Analysis Results

C
Authenticity Grade
28.00%
Fake Reviews
4.60
Original Rating
4.00
Adjusted Rating

Analysis Summary

The reviews show a mix of authentic and potentially suspicious content. While most reviews contain specific, plausible usage scenarios (TV glare reduction, apartment window replacement, office decoration), there are concerning patterns. The product has duplicate reviews (R12IWXLEMHTK3U and R1FVGW0RJ8ZFNH appear twice with identical text), which is highly unusual. The overwhelmingly positive rating distribution (8x 5-star, 2x 4-star, 1x 1-star) with repetitive phrasing about material quality and specific hanging methods suggests some coordinated reviewing. However, the detailed personal stories and varied applications lend credibility to many reviews. The moderate fake percentage reflects these conflicting signals.

Review Statistics

97
Total Reviews on Amazon
-0.60
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 (4.00 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 (4.60 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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