Online reviews have a significant impact on consumer choices on a variety of digital platforms, including e-commerce websites and hotel reservations. However, the existence of false or misleading reviews might mislead users and harm these platforms' trustworthiness. In the fields of machine learning and natural language processing, identifying such misleading opinion spam has grown in importance. This research introduces a machine learning-based framework that uses textual analysis techniques to identify dishonest hotel reviews. In order to prepare the data for model training, the system applies preprocessing techniques, including text cleaning and feature extraction to a dataset of hotel reviews gathered from internet sources. After then, reviews are categorised as honest or misleading using machine learning algorithms. The suggested approach is put into practice as an online tool that lets users access datasets, train algorithms, and determine whether new reviews are genuine. The model obtains an accuracy of roughly 82.81%, according to experimental evaluation, demonstrating the efficacy of the suggested method in detecting misleading opinion spam.
Mahesh et al. (Sun,) studied this question.