Abstract:
The type of speech that takes place online intends to attack an individual or a group based on
religion, ethnicity, gender, disability and even based on the color of their skin. Some popular
social media in Bangladesh notably Facebook and Youtube filled with these types of speeches.
The comment section of celebrities can be a perfect example of people spreading hatred. In
recent times we can also see religious clashes and cases of suicide in Bangladesh because of
the spread of hate speech. Filtering these types of comments and opinions has become a need
to make social media free of negativity. So detecting hate speech in the Bangla language has
been our primary goal. There had been a few previous works, but they were not up to the mark.
A significantly large dataset is being used consisting of more than eight thousand comments
collected from different social media platforms. We Introduced a model that classifies Bangla
comments into normal speech and hates speech by implementing Support Vector Machine
(SVM), Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbor(KNN)
algorithms. Our model with the help of specific calculation provides the most dependable result
in Bangla Language. After analysis results of all algorithm, we choose the best model that is
produced best accuracy for test data.
