Information Security in Social Media Sites: Sentiment Analysis of Email

In today’s digital era, email stands as a fundamental tool for communication due to its convenience, rapidity, flexibility, and comprehensive archival capabilities. However, the escalating risk of cyber threats targeting emails has introduced new challenges. This research introduces an innovative approach to identify fraudulent emails through a hybrid-based approach using sentiment analysis. The proposed method unites the lexicon-based technique using Word2Vec features with a Machine Learning (ML) classification approach. Two prominent feature extraction methods, Bag of Words and TF-IDF, are integrated to enhance the precision of the analysis. The study employed well-established ML classifiers including Random Forest, KNN, Decision Tree, SVM, Multilayer Perception, and logistic model to analyze the emails. The result of the analysis was impressive across the selected models, with Support Vector Machine and Multilayer Perception slightly surpassing others with an impressive 99% accuracy, F1-score, and Recall rate respectively. This emphasizes the effectiveness of this model in recognizing fraudulent emails.