In this new digital era, accessibility to real-world events is moving towards web-based modules. This is mostly visible on e-commerce websites where there is limited availability of physical verification. With this unforeseen development, we depend on the verification in the virtual world to influence our decisions. One of the decision making process is deeply based on review reading. Reviews play an important part in this transactional process. And seeking a real review can be very tenuous work for the user. On the other hand, fake review heavily impacts these transaction records of a product. The article presents an implementation of a Siamese network for detecting fake reviews. The fake reviews dataset, consisting of 40K reviews, preprocessed with different techniques. The cleaned data is passed through embeddings generated by MiniLM BERT for contextual relationship and Word2Vec for semantic relationship to form vectors. Further, the embeddings are trained in a Siamese network with LSTM layers connected to fuzzy logic for decision-making. The results show that fake reviews can be detected with high accuracy on a siamese network for prediction and verification.

翻译：在这个新的数字时代，对现实世界事件的访问正转向基于网络的模块。这在电子商务网站上尤为明显，因为在这些网站上，物理验证的可用性有限。面对这种前所未有的发展，我们依赖虚拟世界中的验证来影响我们的决策。其中一种决策过程深深依赖于阅读评论。评论在这一交易过程中扮演着重要角色。而寻找真实评论对用户来说可能是一项非常繁琐的工作。另一方面，虚假评论严重影响产品的交易记录。本文介绍了一种用于检测虚假评论的孪生网络的实现。虚假评论数据集包含4万条评论，采用了不同的技术进行预处理。清洗后的数据通过MiniLM BERT生成的嵌入向量获取上下文关系，通过Word2Vec生成的嵌入向量获取语义关系，以形成向量。此外，这些嵌入向量在具有LSTM层的孪生网络中进行训练，并连接模糊逻辑进行决策。结果表明，在用于预测和验证的孪生网络上，虚假评论可以被高精度检测。