## 4 Experimental Results
This balance is crucial for accurately identifying offensive content (high precision) while capturing as many offensive instances as possible (high recall). However, when considering OLD, having high F1, especially the weighted average F1, is crucial because it accounts for class imbalances. Based on weighted average F1, the models \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" and \"bert encased preprocess/1 + cetra small/2 + cnn\" have the highest score of 0.90, indicating strong performance in the presence of class imbalances. Therefore, for offensive speech detection, the \"bert encased preprocess/1 + small bert/L4H512A8/1 + neural network layers\" model and the \"bert encased preprocess/1 + cetra small/2 + cnn\" model are the best among our proposed approach. Our innovative approach, fusing features, embedding techniques, and a transformer model, distinguishes our work in the field. The analysis highlights the significance of context in offensive speech detection. Consequently, we incorporated context using various approaches. Models enriched with context proved beneficial in detection tasks. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Methodology** & **Dataset** & **F1-Score** & **Key Features/Limitations** \\ \hline
[63] & J48 graft & 14,000 Tweets & 0.78 & Simple implementation, limited contextual understanding \\ \hline
[31] & LR & 16,000 Tweets & 0.53 (for multiclass), 0.73 (for binary class) & Basic feature engineering, limited to binary classification \\ \hline
[59] & SVM & Davidson Dataset & 0.89 & Good with high-dimensional data, computationally intensive \\ \hline
[64] & RETINA & Davidson Dataset & 0.67 & End-to-end learning, requires large training data \\ \hline
[43] (baseline) & SVM, LSTM, CNN & Task Dataset & 0.69, 0.75, 0.80 & Multiple model comparison, dataset-specific performance \\ \hline
[65] & RoBERTa-Bs, RoBERTa-RT, RoBERTa-TW & Task Dataset & 0.787, 0.816, 0.786 & Strong language modeling, high computational cost \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of performance of our approach with existing models. \begin{table}
\begin{tabular}{p{56.9 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} p{113.8 pt}} \hline \hline
**Pre-Processing Model** & **Model** & \multicolumn{3}{c}{**Macro**} & \multicolumn{3}{c}{**Weighted Avg.**} \\ \hline  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{3}{*}{bert encased preprocess/1} & small bert/L4H512A8/1 & \multirow{3}{*}{0.76} & \multirow{3}{*}{0.70} & \multirow{3}{*}{0.72} & \multirow{3}{*}{0.89} & \multirow{3}{*}{0.90} & \multirow{3}{*}{0.90} \\  & + neural network layers & & & & & & \\ \hline albert en base/2 & albert encoder + cnn & 0.51 & 0.55 & 0.50 & 0.79 & 0.65 & 0.70 \\ \hline bert encased preprocess/1 & small bert/L4H512A8/1 + cnn & \multirow{3}{*}{0.73} & \multirow{3}{*}{0.73} & \multirow{3}{*}{0.73} & \multirow{3}{*}{0.73} & \multirow{3}{*}{0.73} & \multirow{3}{*}{0.73} \\  & + cnn & & & & & & \\ \hline bert encased preprocess/1 & electra small/2 + cnn & 0.75 & 0.73 & 0.74 & 0.89 & 0.90 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the transformer models used.