Sentiment Analysis-Based Sexual Harassment Detection Using Machine Learning Techniques

Despite intensive efforts to prevent and control malicious human activities, they still pose grave risks and significant challenges in cyber-space. Malicious human activities are an evolving problem, especially with fast-growing technological advances. Sexual harassment or cyberbullying is considered an online malicious human activity that can easily affect legitimate users, governments, or other targets. The primary goal of this research is to propose an approach that could be utilized towards developing detection systems and enhance the classification of the different types of malicious human activities by using machine learning with different algorithms. Experiments showed that combining Term Frequency Inverse Document Frequency (TF-IDF) with machine learning achieved 81 % accuracy rate.