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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60886

AI BASED PRODUCT REVIEW RANKING SYSTEM USING NLP AND STATISTICAL CONFIDENCE SCORING

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60886

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem60886

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DMKDB

Dr. M. Kalpana Devi Bai

SLB

S. Leela Bhaskar

KLS

K. Lakshmi Sowjanya

MM

M. Meghana

KNMB

K. Naga Mukund Bhush

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Abstract

– Consumer review systems on e-commerce platforms suffer from critical ranking deficiencies: aggregate star ratings ignore text quality, raw helpfulness vote counts introduce temporal popularity bias and vote sparsity in newly listed products renders rank orderings statistically unreliable. This paper presents a domain-agnostic, time- aware trustworthy review ranking framework whose three-component pipeline can be applied to any structured review dataset containing text, star ratings, helpfulness votes and timestamps. The framework integrates: (i) Wilson Lower Bound (WLB) confidence scoring to quantify community trust under sparse vote conditions; (ii) a Natural Language Processing (NLP) quality module employing VADER sentiment analysis, review length normalization and

KEYWORDS

detection and (iii) a quartile-driven time-decay weighting scheme that privileges recent reviews without discarding historically informative ones. All three components are fused into a weighted hybrid score and implemented in a reproducible Google Colab / Jupyter Notebook environment. Validation is conducted on the publicly available Amazon Kindle Store review corpus (960 000 reviews). Quantitative evaluation using NDCG@10 (0.847) and Precision@10 (0.80) demonstrates that the proposed hybrid framework outperforms all single- dimensional baselines by up to 65.4% while requiring no model training and running to completion in under two minutes on standard hardware

Cite This Article

Dr. M. Kalpana Devi Bai, S. Leela Bhaskar, K. Lakshmi Sowjanya, M. Meghana, K. Naga Mukund Bhush, "AI BASED PRODUCT REVIEW RANKING SYSTEM USING NLP AND STATISTICAL CONFIDENCE SCORING," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026. https://doi.org/10.55041/ijsrem60886

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Publication Date

4/21/2026

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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