The world has now become an ecumenical village because of the Internet. Online platforms like e-commerce sites, search engines, social media have convoluted with the general routine of daily life. Social sites such as Twitter and Facebook have a user population larger than most of the countries, due to which communication is now largely shifted to text-based communication from verbal communication. This research investigates a common yet crucial problem of sarcasm detection in text-based communication. To prevent this problem a novel model has been proposed based on Google BERT (Bidirectional Encoder Representations from Transformers) that can handle volume, velocity and veracity of data. The performance of the model is compared with other classical and contemporary approaches such as Support Vector Machine, Logistic Regression, Long Short Term Memory and Convolutional Neural Network, BiLSTM and attention-based models which have been reported to be used for such tasks. The proposed model establishes its competence by evaluation on different parameters such as precision, recall, F1 score and accuracy. The model is built with the hope that it may help not only the government but also the general public to build a safer and technologically advanced society.

由于互联网的存在，世界现在已成为一个普遍的村庄。诸如电子商务网站，搜索引擎，社交媒体之类的在线平台已与日常生活的常规混为一谈。诸如Twitter和Facebook之类的社交网站的用户数量超过了大多数国家/地区，由于这些原因，现在的交流已从言语交流很大程度上转移到基于文本的交流。这项研究调查了基于文本的交流中嘲讽检测的一个常见但至关重要的问题。为了避免这个问题，已经提出了一种基于Google BERT（来自变压器的双向编码器表示）的新颖模型，该模型可以处理数据的量，速度和准确性。将该模型的性能与其他经典和现代方法（如支持向量机，Logistic回归，据报道，长期短期记忆和卷积神经网络，BiLSTM和基于注意力的模型已用于此类任务。所提出的模型通过评估不同参数（例如精度，召回率，F1得分和准确性）来建立其能力。建立该模型的希望是，它不仅可以帮助政府，而且可以帮助公众建立一个更安全，技术先进的社会。