Paper Key : IRJ************810
Author: Suhas S
Date Published: 09 Jul 2024
Abstract
The Fake Review Detection project aims to direct the growing problem of deceptive reviews that mislead consumers and distort market perceptions. Making use of machine-learning and natural-language-processing (NLP) techniques, this project develops a robust system capable of identifying and classifying fake reviews with high accuracy. Our solution integrates a trained machine learning model, specifically a Naive Bayes classifier, which analyzes the textual content of reviews to determine their authenticity. The model trained using a comprehensive dataset of labeled reviews, allowing it to learn the distinguishing characteristics of genuine and fake reviews. The system is built with a user-friendly web interface, enabling users to input reviews and receive immediate feedback on their authenticity. This interface is developed using Flask for the back end and HTML, CSS, and Bootstrap for the front end, ensuring a responsive and accessible user experience. Key features of the application include real-time review analysis, intuitive navigation, and detailed results presentation. The project also outlines future enhancements, such as incorporating more sophisticated models like deep learning algorithms, expanding the dataset for better model generalization, and integrating multilingual support. By providing an effective tool for fake review detection, this project contributes to maintaining the integrity of online reviews and assisting customers in making informed decisions.Keywords: Fake Reviews, Review Detection, Natural-Language-Understanding, Text Categorization, Sentiment Analysis.