Detection of Fake Online Reviews Using Machine Learning

With the increasing influence of online reviews on consumer choices, detecting fake reviews has become a priority. As misleading content spreads across the internet, businesses and researchers are focusing on finding reliable ways to identify and filter out fake reviews. This study employs the TF-IDF technique to extract relevant features from a dataset and tests three machine learning models to determine their efficiency in detecting fraudulent reviews. The findings are compared to previous research to evaluate the models' effectiveness and accuracy in identifying fake reviews. Keywords: Fake review detection, machine learning, TF-IDF, Naive Bayes, Support Vector Machine (SVM), Logistic Regression.