## 7 Experiments
### Comparison with Other Models from State-of-the-Art  
To compare the models' performance with other similar approaches presented in the literature, we have to consider several aspects. There are numerous studies, in which authors transformed the Kaggle toxic comments competition dataset labels to the binary values (representing toxic and non-toxic values) and solved the binary classification task of toxicity detection [35; 65]. On the other hand, when solving the task as a multi-label classification, comparison of these approaches could still be inconsistent, as the evaluation of the models in the literature is not coherent, as multiple studies using different data for evaluation, e.g., a subset of training data (split to 80/20 train/test ratio), or separate labelled testing data, which became available later. Therefore, to compare with the different models, different evaluation of the models had to be implemented. To compare with the results presented in [11; 36], we needed to compute the averaged precision and recall values for each class (as a separate classification problems, contrary to multi-label evaluation in previous experiments), and then compute the F1 metric. Overall performance of our approach is compared with several similar approaches using the accuracy, precision, recall, F1 score. Table 19 summarizes the evaluation metrics of the models.  
To analyze how well the models perform on a particular class, we compared the results with other similar approaches described in [66]. Table 20 summarizes and compares our best-performing model with the best performing related models. This time, to be able to compare with the models from the study, we used the accuracy score computed for the specific categories. Please note that the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** & **MCC** & **Support** \\ \hline
**toxic** & 0.59 & 0.85 & 0.70 & 0.67 & 6090 \\
**severe\_toxic** & 0.33 & 0.66 & 0.44 & 0.46 & 367 \\
**obscene** & 0.61 & 0.78 & 0.69 & 0.67 & 3691 \\
**threat** & 0.47 & 0.26 & 0.34 & 0.35 & 211 \\
**insult** & 0.69 & 0.68 & 0.68 & 0.66 & 3427 \\
**identity\_hate** & 0.76 & 0.43 & 0.55 & 0.56 & 712 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Results of the individual toxicity classes of **optimized** biLSTM + CNN model with pre-trained GloVe Twitter embeddings without standard pre-processing.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline
**BiGRU (GloVe)**[11] & 0.73 & 0.85 & 0.772 \\
**BiGRU + Attention (GloVe)**[11] & 0.73 & 0.87 & 0.779 \\
**BiGRU + Attention (fastText)**[11] & 0.74 & 0.87 & 0.783 \\
**Ensemble model**[11] & 0.74 & 0.88 & 0.791 \\ \hline
**BiLSTM + CNN + GloVe(Twitter) + No PP** & 0.78 & 0.79 & 0.789 \\
**BiLSTM + CNN + GloVe(CC) + No PP** & 0.75 & 0.84 & 0.796 \\
**bare BERT-BASE uncased** & 0.78 & 0.83 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Comparison of the performance metrics of the proposed approaches with similar models.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** & **MCC** & **Support** \\ \hline
**toxic** & 0.64 & 0.81 & 0.71 & 0.69 & 6090 \\
**severe\_toxic** & 0.30 & 0.64 & 0.41 & 0.43 & 367 \\
**obscene** & 0.67 & 0.75 & 0.71 & 0.69 & 3691 \\
**threat** & 0.60 & 0.39 & 0.47 & 0.48 & 211 \\
**insult** & 0.64 & 0.75 & 0.69 & 0.67 & 3427 \\
**identity\_hate** & 0.60 & 0.71 & 0.65 & 0.64 & 712 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Results of the individual toxicity classes of optimized and fine-tuned bare BERT uncased model.  
table contains the results presented in one-vs-all approach (e.g., each class compared to the rest of the others), computed from individual confusion matrices for each specific category.