Enhancing Email Communication Security through Hierarchical Machine Learning Models

Abstract

With the exponential growth of digital communication, the menace of spam emails has become a pervasive issue, threatening the efficiency and security of communication channels. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection. This paper presents a comprehensive approach to designing and implementing an advanced spam detection system that leverages the power of machine learning and Natural Language processing(NLP)techniques.

Our focus in this article is to detect any deceptive text reviews. In order to achieve that we have worked with both labelled and unlabelled data and proposed Techniques such as Tokenization Stemming, Lemmatization, Tf-idf Vectorization and ML Algorithm such as Naïve Bayes, Random Forest, KNN, Support Vector Machine.