  While in real life everyone behaves themselves at least to some extent, it is
much more difficult to expect people to behave themselves on the internet,
because there are few checks or consequences for posting something toxic to
others. Yet, for people on the other side, toxic texts often lead to serious
psychological consequences. Detecting such toxic texts is challenging. In this
paper, we attempt to build a toxicity detector using machine learning methods
including CNN, Naive Bayes model, as well as LSTM. While there has been
numerous groundwork laid by others, we aim to build models that provide higher
accuracy than the predecessors. We produced very high accuracy models using
LSTM and CNN, and compared them to the go-to solutions in language processing,
the Naive Bayes model. A word embedding approach is also applied to empower the
accuracy of our models.
