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@article{171357,
        author = {Priyanshu Singh and Priyanka Yadav and Nidhi Saxena},
        title = {Leveraging Machine Learning for Authentic Review Verification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3356-3361},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171357},
        abstract = {E-commerce sites are growing fast, bringing with them an explosion in user-generated content such as online reviews that shape the purchases people make. Unfortunately, fake reviews – deliberately misleading feedback intended to influence opinions – are becoming an increasingly serious problem. They destroy trust and hurt businesses. Traditional approaches, such as checking reviews manually or through simple rule-based systems, are no longer adequate to cope with the volume and complexity of such fake reviews. The core of this study will involve the use of ML and NLP techniques for an efficient review-fake detector based on the Fake reviews Dataset. Comparative analysis of various models including SVMs, Naive Bayes, and even complex deep learning architecture such as LSTM is provided with a view to understand what features distinguish linguistic and textual patterns between authentic and deceitful reviews. In addition to this, additional metadata features, such as reviewer behavior and temporal trends, enhance the accuracy of detection. The findings suggest the possibility of using machine learning to combat fake reviews and call for strong, scalable solutions to maintain trust and integrity in the online ecosystem.},
        keywords = {component, formatting, style, styling, insert (key words)},
        month = {December},
        }