Published Paper Details:

The surge in online reviews has revolutionized consumer decision-making, accompanied by the pervasive issue of fake online reviews. This research paper delves into a robust machine learning approach for combating this challenge. The paper addresses the crucial need for dependable methods to distinguish genuine user feedback from deceptive ones, fostering trust in online commerce and informed consumer decisions. Leveraging a diverse dataset of reviews, the study employs a multi-faceted methodology encompassing data preprocessing, feature extraction, and machine learning classification algorithms. Techniques such as text processing, sentiment analysis, and advanced classification models are utilized to assess their effectiveness in identifying fake reviews across diverse industries and platforms. The paper's outcomes highlight the relative performance of various machine learning algorithms, including Random Forests, Support Vector Machines, Logistic Regression, and more. The evaluation employs precision, recall, F1-score, and accuracy metrics to gauge predictive accuracy. These findings provide insights into the strengths and limitations of each algorithm, aiding practitioners in selecting the most appropriate model for their specific context. Concluding with the broader implications, the research underscores the significance of a machine learning-driven approach in countering fake reviews and elevating the credibility of online platforms. Serving as a valuable contribution to academia and industry, this research paper equips stakeholders with insights to promote more authentic and trustworthy digital interactions.