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.

翻译：在当今数字化环境中，在线评论已成为各类企业推广产品和服务不可或缺的工具。营销人员、广告商及在线企业有动机为其产品创作虚假正面评论，同时对其竞争对手的产品撰写负面评论。因此，撰写虚假评论已成为企业自我推广或打压竞争对手的普遍做法。识别此类虚假评论已成为一个持续且深入的研究领域。本文提出一种机器学习模型，旨在识别虚假评论，重点关注餐饮领域。本研究对名为"虚假观点垃圾语料库"的餐厅评论数据集进行了大量实验，深入分析了其性能表现。为此，我们开发了n-gram模型与最大特征数方法，以有效识别虚假内容，尤其针对伪造评论。通过基准研究，我们探索了两种不同特征提取技术的性能，并将其与五种不同的机器学习分类算法进行结合。实验结果表明，被动攻击分类器在文本分类与虚假评论识别方面均表现出最高准确率，显著优于其他算法。此外，本研究还探讨了数据增强方法，并应用多种深度学习技术以进一步改进虚假评论检测流程。研究结果揭示了所提机器学习方法的有效性，并为处理在线商业领域的虚假评论提供了宝贵见解。