# Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation
## 4 Experimentation
### Comparison with Baselines  
We used the following baseline methods for comparison to comprehensively evaluate the performance of our method. **SVM. ** This method uses statistical features and linear support vector machine as a classifier to detect offensive language [2,11]. **CNN. ** This method uses a convolutional layer and maximum pooling to detect offensive language [29]. **Bi-GRU. ** This method uses a bidirectional gated recurrent unit network to extract the semantic features of the text [29]. **Bi-GRU-CNN. ** This method uses hybrid Bi-GRU-CNN for offensive-language detection [29]. **Bi-LSTM. ** This method uses the bidirectional long- and short-term memory network to obtain semantic features of the text and to detect offensive language [29]. **Bi-LSTM-Att. ** In addition to Bi-LSTM, this method adds an attention mechanism to extract text semantic features [30]. **BERT. ** This model uses a fine-tuning BERTbase for offensive-language detection [31,32]. **BERT-CNN. ** This method uses hybrid BERT-CNN for offensive-language detection [31]. **SKS. ** This method detects offensive language based on emotional knowledge sharing [33]. **MSE. ** MSF is our proposed model, which detects offensive language based on multi-semantic fusion. The results of the comparisons are listed in Table 3. From the results, we observe that:  
(1) The performance of traditional machine-learning methods based on the bag-of-words model was not satisfactory. Using only n-grams character features and shallow semantic features cannot well characterize offensive language. The experimental results show that the performance of SVM is quite different from mainstream deep-learning methods, and its generalization ability is weak. (2) The performance of RNN is better than CNN. After the introduction of the attention mechanism, the performance was further improved, and a deeper hierarchical network structure, such as BiGRU-CNN, can better extract deep semantic features. (3) BERT uses the transformer encoder structure, which can capture longer-distance dependencies. Therefore, it is more efficient than RNN and CNN, and its performance was generally improved by about 1% on offensive-language-detection tasks. Based on BERT, after adding the CNN layer, the performance of the model was further improved. (4) [PERSON] et al. [33] adopted a multi-task learning method and introduced more sentiment features. Compared with other methods, SKS performance was significantly improved. However, this model needs to use a significant amount of external sentiment resources, and the model structure is complicated. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & \multicolumn{2}{c}{**Davidson Dataset**} & \multicolumn{2}{c}{**OLID**} \\ \cline{2-5}  & **Acc** & **F1 (weighted)** & **Acc** & **F1 (macro)** \\ \hline SVM \({}^{*}\) & - & 0.870 & - & 0.690 \\ CNN \({}^{*}\) & 0.903 & 0.892 & 0.710 & 0.630 \\ BiGRU \({}^{*}\) & 0.907 & 0.901 & 0.780 & 0.740 \\ BiLSTM \({}^{*}\) & 0.895 & 0.888 & 0.780 & 0.730 \\ BiGRU-CNN \({}^{*}\) & 0.914 & 0.892 & 0.710 & 0.630 \\ BiLSTM-Att \({}^{*}\) & 0.895 & 0.893 & 0.826 & 0.768 \\ BERT \({}^{*}\) & - & 0.910 & 0.839 & 0.798 \\ BERT-CNN \({}^{*}\) & - & 0.920 & - & - \\ SKS \({}^{*}\) & 0.951 & 0.963 & - & - \\ MSF & **0.964** & **0.972** & **0.894** & **0.864** \\ \hline \hline \end{tabular} Note: The results with superscript \({}^{*}\) are imported from the literature.  
\end{table} 
Table 3: The performance of the proposed method MSF and other baseline methods are compared. (5) MSF, our proposed method, achieved the best performance on both datasets. For the DV dataset, MSF improved upon ordinary BERT by 6.2% for weighted F1 and BERT-CNN by 5.2%. Even compared with the strong baseline SKS, the performance of our model was superior by 0.9%. For the OLID dataset, MSF improved upon ordinary BERT by 6.6%.