The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount of data as it is expensive and time consuming. Thus, it is important to automatically process the online user contents to detect and remove hate speech from online media. Many recent approaches suffer from interpretability problem which means that it can be difficult to understand why the systems make the decisions they do. Through this work, some solutions for the problem of automatic detection of hate messages were proposed using Support Vector Machine (SVM) and Na\"ive Bayes algorithms. This achieved near state-of-the-art performance while being simpler and producing more easily interpretable decisions than other methods. Empirical evaluation of this technique has resulted in a classification accuracy of approximately 99% and 50% for SVM and NB respectively over the test set. Keywords: classification; hate speech; feature extraction, algorithm, supervised learning

该研究主要通过使用支持向量机（SVM）和朴素贝叶斯算法，提出了一些解决自动检测仇恨言论问题的方法，并在实证评估中取得了近乎最佳的分类准确性。

使用 SVM 和 Naive BAYES 进行仇恨言论分类