Abstract:
One of the significant wonders of modern technology is social networking platforms. This
gives a major acceleration to the field of communication. With such good advantages of
this technology this has a darker side too. Nowadays people are often seen trolling each
other online. There is also a new roasting culture in online platforms which arrived a few
years ago, where people are often downgrading other people without any moral
consideration. Cyberbullying nowadays has become a trend. People are now just waiting
for a new topic to arrive in social media so that they can make fun of each other. So we are
doing research on this field so that we can make an automated system which would be able
to detect cyberbullying instances on social platforms using textual analysis with the help
of machine learning and artificial intelligence. We are using social media comments in
Bangla language from Facebook for this research along side we are also taking a dataset
from kaggle. Our personal Bangla comments dataset contains 5053 Bengali comments.
These comments are initially categorized as bully or non-bully. And the bully comments
are further categorized as Sexual, Religious, Threat, and Troll and Political comments. The
kaggle dataset also contains Bengali social media comments categorized as Neutral,
Political, Sexual, troll and threat. We have run experiments on both the dataset individually
and combining them together. We have used both machine learning and deep learning
architecture for our experiments. We have achieved highest accuracy of 88% on binary
class classification on the kaggle dataset. And highest 65% on multiclass classification on
the kaggle dataset as well.
