## 2 Literature Review
They then translated these English remarks into Bengali utilizing two techniques: (i) Bengali translation directly; (ii) Bengali translation using dictionaries. [PERSON] et al. evaluated the performance of their system after the translations. Their system impressively achieved the highest accuracy of 0.8057, demonstrating its potency in identifying abusive content in the context of the Bengali language. [PERSON] et al. [33] suggested a method that makes use of a root-level algorithm and unigram string features to identify abusive Bangla comments. They gathered 300 comments for their dataset from a variety of websites, including Facebook pages, news websites, and YouTube. The dataset was split into three subsets, each of which contained 100, 200, and 300 comments. These subsets were used to test their system, which resulted in an average accuracy score of 0.689. [PERSON] et al. [34] carried out a study on detecting hate speech in Bengali and Romanized Bengali. They extracted samples from Twitter in order to gather the necessary information, producing a dataset with 5071 samples in Bengali and Romanized Bengali. They used a variety of training models in their study, including XML-RoBERTa, MuRIL, m-BERT, and IndicBERT. Following testing, they discovered that XML-RoBERTa had the highest accuracy, at 0.796. [PERSON] [35] collected 7245 YouTube reviews manually and divided them into two categories: vulgar and non-vulgar. The purpose of this process was to produce two benchmark corpora for assessing vulgarity detection algorithms. Following the testing of several methods, the bidirectional long short-term memory (BiLSTM) model showed the most promising results, achieving the highest recall scores for identifying vulgar content in both datasets. [PERSON] et al. [36] created a dataset by using online comment scraping tools to collect comments from public Facebook pages, such as news and celebrity pages. SVM, random Forest, and AdaBoost were the three machine learning techniques used to categorize the comments for the detection of abusive content. Their approach, which was based on the random forest classifier, outperformed other methods in terms of accuracy and precision, scoring 0.7214 and 0.8007, respectively. AdaBoost, on the other hand, demonstrated the best recall performance, earning a score of 0.8131. [PERSON] et al. [37] collected a dataset sourced from Facebook, categorized into six distinct classes. The dataset was enriched with linguistic and quantitative features, and the researchers employed a range of text preprocessing techniques, including punctuation removal, elimination of bad characters, handling hashtags, URLs, and mentions, as well as tokenization and stemming. They utilized neural networks, specifically GRUs (gated recurrent units), alongside other machine learning classifiers, to conduct classification tasks based on the historical, religious, cultural, social, and political contexts of the data. [PERSON] et al. [38] used a combination of machine learning classifiers and deep neural networks to detect hate speech in Bengali. They analyzed datasets containing comments from Facebook, YouTube, and newspaper websites using a variety of models, including logistic regression, SVM, CNN, and Bi-LSTM. The researchers divided hate speech into four distinct categories: political, religious, personal, and geopolitical. With F1 scores of 0.78 for political hate speech, 0.91 for personal hate speech, 0.89 for geopolitical hate speech, and 0.84 for religious hate speech detection in the Bengali language, their results showed satisfying performance. [PERSON] [39] created a transliterated corpus of 3000 comments from Bengali, 1500 of which were abusive and 1500 of which were not. As a starting point, they used a variety of supervised machine learning methods, such as deep learning-based bidirectional long short-term memory networks (BiLSTM), support vector machines (SVM), logistic regression (LR), and random forest (RF).