  The challenge of automatic detection of toxic comments online has been the
subject of a lot of research recently, but the focus has been mostly on
detecting it in individual messages after they have been posted. Some authors
have tried to predict if a conversation will derail into toxicity using the
features of the first few messages. In this paper, we combine that approach
with previous work on toxicity detection using sentiment information, and show
how the sentiments expressed in the first messages of a conversation can help
predict upcoming toxicity. Our results show that adding sentiment features does
help improve the accuracy of toxicity prediction, and also allow us to make
important observations on the general task of preemptive toxicity detection.
