  Social networking sites (SNSs) facilitate the sharing of ideas and
information through different types of feedback including publishing posts,
leaving comments and other type of reactions. However, some comments or
feedback on SNSs are inconsiderate and offensive, and sometimes this type of
feedback has a very negative effect on a target user. The phenomenon known as
flaming goes hand-in-hand with this type of posting that can trigger almost
instantly on SNSs. Most popular users such as celebrities, politicians and news
media are the major victims of the flaming behaviors and so detecting these
types of events will be useful and appreciated. Flaming event can be monitored
and identified by analyzing negative comments received on a post. Thus, our
main objective of this study is to identify a way to detect flaming events in
SNS using a sentiment prediction method. We use a deep Neural Network (NN)
model that can identity sentiments of variable length sentences and classifies
the sentiment of SNSs content (both comments and posts) to discover flaming
events. Our deep NN model uses Word2Vec and FastText word embedding methods as
its training to explore which method is the most appropriate. The labeled
dataset for training the deep NN is generated using an enhanced lexicon based
approach. Our deep NN model classifies the sentiment of a sentence into five
classes: Very Positive, Positive, Neutral, Negative and Very Negative. To
detect flaming incidents, we focus only on the comments classified into the
Negative and Very Negative classes. As a use-case, we try to explore the
flaming phenomena in the news media domain and therefore we focused on news
items posted by three popular news media on Facebook (BBCNews, CNN and FoxNews)
to train and test the model.
