Fake Feedback Detection Using Machine Learning

Abstract

The prevalence of false feedback increases the reliance on online media for information and interaction, which is a major challenge for businesses, consumers and reputation management. This project presents a new way to detect false positives using machine learning techniques. We propose a multi-modal model that uses natural language processing (NLP) and supervised learning algorithms to analyze text response data. Our methodology includes sentiment analysis, the extraction of language features and behavioral patterns to distinguish between genuine feedback and fake news. We evaluate our model using a comprehensive data set that includes real and synthetic feedback samples. We are Analyzing that our approach which we are going to implement in future that can achieves high accuracy and robustness and is significantly better than traditional detection methods. In addition, we discuss the implications of our findings for increasing trust in online reviews and the potential for feedback monitoring. This initiative will contribute to the growing digital presence and provide a scalable solution for stakeholders seeking to reduce the impact of false positives in various domains.