## 2 Literature Review
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Paper** & **Classifier** & **Highest Score** & **Language** & **Sample Size** & **Class and Ratio** & **Data Sources** \\ \hline
[29] & \
\begin{tabular}{l} Multinomial Naive Bayes, \\ Random Forest, \\ Support Vector Machines, \\ \end{tabular} &
\begin{tabular}{l} 80\% \\ (Accuracy) \\ \end{tabular} & Bengali & 2.5K & - & F \\ \hline
[30] & \begin{tabular}{l} Support Vector Machines, \\ Naive Bayes, \\ Decision Tree, \\ K-Nearest Neighbors \\ \end{tabular} & \
\begin{tabular}{l} 97\% \\ (Accuracy) \\ \end{tabular} & Bengali & 2.4 K &
\begin{tabular}{l} Non-Bullying \\ Bullying (10\%) \\ \end{tabular} & F, T \\ \hline
[31] & \begin{tabular}{l} Linear Support Vector \\ Classification, \\ Logistic Regression, \\ Multinomial Naive Bayes, \\ Random Forest \\ Artificial Neural Network, \\ RNN + LSTM \\ \end{tabular} & \begin{tabular}{l} 82.2\% \\ (Accuracy) \\ \end{tabular} & Bengali & 4.7 K & \
\begin{tabular}{l} Slang (19.57\%), \\ Religious, \\ Politically, \\ Positive, \\ Neutral, \\ violated (13.28\%), \\ Anti- \\ \end{tabular} &
\begin{tabular}{l} F, Y, \\ News \\ portal \\ \end{tabular} \\ \hline
[32] & Naive Bayes & \
\begin{tabular}{l} 80.57\% \\ (Accuracy) \\ \end{tabular} & Bengali & 2.665 K &
\begin{tabular}{l} Non-Abusive, \\ Abusive (45.55\%) \\ \end{tabular} & Y \\ \hline
[33] & \begin{tabular}{l} Root-Level \\ approach \\ \end{tabular} & \begin{tabular}{l} 68.9\% \\ (Accuracy) \\ \end{tabular} & Bengali & 300 & \
\begin{tabular}{l} Not Bullying, \\ Bullying \\ \end{tabular} &
\begin{tabular}{l} F, Y \\ News \\ portal \\ \end{tabular} \\ \hline
[35] & \begin{tabular}{l} Logistic Regression, \\ Support Vector Machines, \\ Stochastic Gradient Descent, \\ Bidirectional LSTM \\ \end{tabular} & \
\begin{tabular}{l} 89.3\% (F1 \\ Score) \\ \(82.4\%\) (F1 \\ Score) \\ \end{tabular} & Bengali & 7.245 K &
\begin{tabular}{l} Non Vulgar, \\ Vulgar \\ \end{tabular} & Y \\ \hline
[36] & \begin{tabular}{l} Support Vector Machines, \\ RF, \\ Adaboost \\ \end{tabular} & \
\begin{tabular}{l} 72.14\% \\ (Accuracy) \\ 80\% \\ (Precision) \\ \end{tabular} & Bengali & 2 K &
\begin{tabular}{l} Non Abusive, \\ Abusive (78.41\%) \\ \end{tabular} & F \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research on vulgarity detection or related topics in Bengali (Facebook (F), YouTube (Y)). Based on our comprehensive analysis of papers related to vulgarity detection and related topics like abusive and bullying detection, as well as detection in the low-resource language Bengali, several critical research gaps emerge. These gaps include the absence of a clear problem definition in some papers, the prevalence of small-sized datasets without a well-defined annotation process, and the lack of benchmarking efforts to assess dataset  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper Classifier** & **Highest Score** & **Language** & **Sample Size** & **Class and Ratio** & **Data Sources** \\ \hline  & & & & Religious comment (14.9\%), & & \\  & & & & Hate (19.2\%), & & \\  & & & & speech (19.2\%), & & \\  & & & & Incteful (10.77\%), & & \\  & & & & & Incteful (10.77\%), & \\  & & & & & Communal (10.77\%), & \\  & & & & & Intured (15.67\%), & & \\  & & & & Religious & & \\  & & & & hatred (15.68\%), & & \\  & & & & & Incteful (15.68\%), & & \\  & & & & & Political (10.77\%), & & \\  & & & & & comment (23.43\%) & & \\  
quality. Additionally, class imbalance in datasets remains an issue, and limited attention has been given to vulgarity detection in low-resource language Bengali, with only a single work [35] addressing this area. Many papers fail to specify the source of their datasets and conduct limited experiments. Field surveys are often superficial or nonexistent. Furthermore, none of the papers considered ethical considerations in data collection, such as preserving user privacy through dataset anonymization.