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：在当代数字环境中，在线评论已成为各类企业推广产品与服务不可或缺的工具。营销人员、广告商及线上企业为谋取利益，有动机为其产品撰写欺骗性正面评论，同时针对竞争对手产品发布负面评价。因此，撰写欺骗性评论已成为企业自我推广或削弱对手的普遍做法。识别此类欺骗性评论已成为一个持续且活跃的研究领域。本研究论文提出了一种机器学习模型用于识别欺骗性评论，尤其聚焦于餐饮领域。研究深入分析了在名为“欺骗性观点垃圾语料库”的餐厅评论数据集上开展的多项实验结果。为实现目标，研究构建了n元语法模型与最大特征方法，以有效识别欺骗性内容（重点关注虚假评论）。通过基准研究，比较了两种不同特征提取技术的性能，并将其与五种机器学习分类算法相结合。实验结果表明，被动攻击分类器在多种算法中表现突出，不仅在文本分类任务中展现出最高准确率，在识别虚假评论方面也尤为出色。此外，研究还探讨了数据增强技术，并应用多种深度学习方法来进一步提升欺骗性评论检测效果。相关发现揭示了所提机器学习方法的有效性，并为处理线上商业领域的欺骗性评论提供了宝贵见解。