  In the contemporary digital landscape, online reviews have become an
indispensable tool for promoting products and services across various
businesses. Marketers, advertisers, and online businesses have found incentives
to create deceptive positive reviews for their products and negative reviews
for their competitors' offerings. As a result, the writing of deceptive reviews
has become an unavoidable practice for businesses seeking to promote themselves
or undermine their rivals. Detecting such deceptive reviews has become an
intense and ongoing area of research. This research paper proposes a machine
learning model to identify deceptive reviews, with a particular focus on
restaurants. This study delves into the performance of numerous experiments
conducted on a dataset of restaurant reviews known as the Deceptive Opinion
Spam Corpus. To accomplish this, an n-gram model and max features are developed
to effectively identify deceptive content, particularly focusing on fake
reviews. A benchmark study is undertaken to explore the performance of two
different feature extraction techniques, which are then coupled with five
distinct machine learning classification algorithms. The experimental results
reveal that the passive aggressive classifier stands out among the various
algorithms, showcasing the highest accuracy not only in text classification but
also in identifying fake reviews. Moreover, the research delves into data
augmentation and implements various deep learning techniques to further enhance
the process of detecting deceptive reviews. The findings shed light on the
efficacy of the proposed machine learning approach and offer valuable insights
into dealing with deceptive reviews in the realm of online businesses.

Information Retrieval;Artificial Intelligence;Machine Learning