AI BASED PRODUCT REVIEW RANKING SYSTEM USING NLP AND STATISTICAL CONFIDENCE SCORING
¹ Dr. M. Kalpana Devi Bai
Associate Professor
Department of Computer Science and Engineering
PSCMR College Of Engineering And Technology, Vijayawada
dkalpananaik@gmail.com
² S. Leela Bhaskar, ³ K. Lakshmi Sowjanya, ⁴M. Meghana, 5K. Naga Mukund Bhush
Department of Computer Science and Engineering
PSCMR College Of Engineering And Technology, Vijayawada, NTR District
Andhra Pradesh, India — 520001
¹ leelabhaskar736@gmail.com,²sowjanyakunala14@gmail.com, ³ mamillameghana19@gmail.com ,
⁴ mukundbhushan07@gmail.com
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 keyword 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.
Keywords — Review Ranking, Helpfulness Prediction, Wilson Lower Bound, VADER Sentiment Analysis, Time- Decay Weighting, NDCG, Precision@K, Amazon Kindle, NLP, E-commerce, Google Colab
