Sentiment Analysis in Detecting Sophistication and Degradation Cues in Malicious Web Contents

Mobility, ease of accessibility, and portability have continued to grant ease in the adoption rise of smartphones; while, also proliferating the vulnerability of users that are often susceptible to phishing. With some users classified to be more susceptible than others resulting from media presence and personality traits, many studies seek to unveil lures and cues as employed by these attacks that make them more successful. Web content has been often classified as genuine and malicious. The study seeks to effectively identify cues and lures that will help users classify content as malicious in phishing attacks using sentiment analysis. The machine learning of choice is the XGBoost. Results show that the ensemble yields a prediction accuracy of 97 percent.