论文标题
使用多项式幼稚贝叶斯和优化的线性支撑向量机器的机器学习算法的AI动力抗周期欺凌系统
AI Powered Anti-Cyber Bullying System using Machine Learning Algorithm of Multinomial Naive Bayes and Optimized Linear Support Vector Machine
论文作者
论文摘要
“除非并且直到我们的社会认识到网络欺凌行为，否则成千上万的沉默受害者的苦难将继续。” 〜安娜·玛丽亚·查韦斯（Anna Maria Chavez）。关于网络欺凌的一系列研究无法为网络欺凌提供可靠的解决方案。在这项研究工作中，我们能够通过开发能够以92％精度来检测和拦截欺凌传入和传出消息的模型来为此提供永久解决方案。我们还开发了一种聊天机器人自动化消息系统，以测试我们的模型，从而使用多项式幼稚贝叶斯（MNB）的机器学习算法和优化的线性支持向量机（SVM）开发了人工智能供电的反周期欺凌系统。我们的模型能够检测并拦截欺凌传出和传入的欺凌信息并立即采取行动。
"Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue." ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naive Bayes (MNB) and optimized linear Support Vector Machine (SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action.
