Bangla-Hate-Speech-Detection

This Project aims to train different models that can detect Bengali hate speech on different social media platforms and do a comparative analysis of the models. Using statistical methods, the collected data were analyzed. Also, manually labeled the collected data based on sentiment. To remove punctuation marks, a punctuation remover is used, and regular expressions are used to remove foreign languages from the dataset. Moreover, Bangla natural language toolkit was used to remove Bangla stopwords from the data. A label encoding method is used to make the dataset machine readable. Natural language processing toolkit porter stemmer is used for tokenization and for feature extraction, term frequency-inverse document frequency is used for training and testing the dataset, hold-out validation approach was used. Several machine learning and recurrent neural network models, decision tree (DT), K-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), multinomial naïve bayes (MNB), long short term memory (LSTM), bidirectional long short term memory (Bi-LSTM), and convolutional long short term memory (CNN-LSTM) were implemented