  The abstract outlines the problem of toxic comments on social media
platforms, where individuals use disrespectful, abusive, and unreasonable
language that can drive users away from discussions. This behavior is referred
to as anti-social behavior, which occurs during online debates, comments, and
fights. The comments containing explicit language can be classified into
various categories, such as toxic, severe toxic, obscene, threat, insult, and
identity hate. This behavior leads to online harassment and cyberbullying,
which forces individuals to stop expressing their opinions and ideas. To
protect users from offensive language, companies have started flagging comments
and blocking users. The abstract proposes to create a classifier using an
Lstm-cnn model that can differentiate between toxic and non-toxic comments with
high accuracy. The classifier can help organizations examine the toxicity of
the comment section better.
