Abstract: The research explores the challenge of identifying fake reviews, utilizing machine learning and natural language processing. It examines diverse methodologies, including deep learning and linguistic analysis. Categories of deceptive reviews are scrutinized, such as those from competitors or employees. The study addresses associated costs for businesses and impacts on consumer trust. Challenges like natural language mimicry and skilled deception are acknowledged. It emphasizes the necessity for advanced strategies to combat fraudulent reviews effectively, aiming to bolster trust and accuracy in the digital realm.

Keywords: Fake product reviews, Deceptive reviews, Fraudulent reviews, Machine learning, Natural language processing, Deep learning models.

Cite:
Yashaswini Urs, Raghavendra R, "IMPROVING FAKE PRODUCT DETECTION THROUGH A PRIORITY-BASED FEATURE VECTOR APPROACH IN MACHINE LEARNING", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 3, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11302.

| DOI: 10.17148/IARJSET.2024.11302