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Hate Speech Detection Using Machine Learning In Bengali Languages
Islam, M., Akhter, N., Hossain, M.S.,

Hate speech is a common problem in the current time of social media and the internet as it is very easy to be in touch with everything through the internet and social media. Hate speech detection research is not very rare but in terms of Bengali language there are very few works related to hate speech in Bengali language. The proposed research experiment has developed a machine learning based project to detect hate speech from Bengali language data or comments, posts in social media that are in Bengali language. This research work has used 3006 pure Bengali data from social media pages (such as Facebook, YouTube) groups, comment sections of news portals. Further, this research work has categorized them in 0 for non-Hate-Speech and 1 for Hate-Speech to classify the data between non-abusive and abusive data. This research work has used several algorithms to find the best possible result in order to determine whether the sentence is abusive or non-abusive such as Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, K Nearest Neighbor Classifier. From these algorithms, the best result for detecting non-abusive data is the Random Forest [RF] algorithm, which is 67%. © 2022 IEEE.

Journal or Conference Name
Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022
Publication Year