Paper Title
AUTHENTIC FEEDBACK VALIDATION FRAMEWORK
Article Identifiers
: Authors
Keywords
: identifying fraudulent online reviews, learning machines, analyzing latent semantics (LSA), identifying anomalies.
Abstract
Due to the rapid expansion of online services and e-commerce, decision-making now strongly depends on customer feedback. The widespread fraudulent manipulation of online reviews poses a severe danger to consumer confidence and corporate integrity.The Authentic Feedback Validation Framework (AFVF) uses machine learning techniques, namely Latent Semantic Analysis (LSA), to detect dishonest tendencies and enhance the authenticity of evaluations in order to get around this problem.This technology systematically identifies fraudulent activities, including biased user promotions, IP address anomalies, review floods, simultaneously similar reviews, and irrelevant comments, using advanced data analysis. By integrating anomaly identification, textual coherence evaluation, and behavioral monitoring, AFVF provides an automated technique for eliminating manipulated reviews. The system combines sentiment analysis, statistical modeling, and Natural Language Processing (NLP) to assess review trustworthiness.
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How To Cite (APA)
SHEIK ASKAR P, DR.R.TINO MERLIN, MUTHUSAMY R, & SAFFIN VARGESH B (April-2025). AUTHENTIC FEEDBACK VALIDATION FRAMEWORK. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), a857-a867. https://ijnrd.org/papers/IJNRD2504099.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : a857-a867
Other Publication Details
Paper Reg. ID: IJNRD_305034
Published Paper Id: IJNRD2504099
Downloads: 000121986
Research Area: Science and Technology
Author Type: Indian Author
Country: TIRUNELVELI, TAMILNADU, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504099.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504099
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