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In the contemporary digital landscape, online reviews have become anindispensable tool for promoting products and services across variousbusinesses. Marketers, advertisers, and online businesses have found incentivesto create deceptive positive reviews for their products and negative reviewsfor their competitors' offerings. As a result, the writing of deceptive reviewshas become an unavoidable practice for businesses seeking to promote themselvesor undermine their rivals. Detecting such deceptive reviews has become anintense and ongoing area of research. This research paper proposes a machinelearning model to identify deceptive reviews, with a particular focus onrestaurants. This study delves into the performance of numerous experimentsconducted on a dataset of restaurant reviews known as the Deceptive OpinionSpam Corpus. To accomplish this, an n-gram model and max features are developedto effectively identify deceptive content, particularly focusing on fakereviews. A benchmark study is undertaken to explore the performance of twodifferent feature extraction techniques, which are then coupled with fivedistinct machine learning classification algorithms. The experimental resultsreveal that the passive aggressive classifier stands out among the variousalgorithms, showcasing the highest accuracy not only in text classification butalso in identifying fake reviews. Moreover, the research delves into dataaugmentation and implements various deep learning techniques to further enhancethe process of detecting deceptive reviews. The findings shed light on theefficacy of the proposed machine learning approach and offer valuable insightsinto dealing with deceptive reviews in the realm of online businesses.

Unmasking Falsehoods in Reviews: An Exploration of NLP Techniques