## 5 Discussion and Future Work  
Since the goal is to detect messages belonging to class \"1\", comparing the F1-Score values shown in Table 6, we see that the ML models perform slightly worse than the Transformer model, but essentially at the same level. We can also look at the AUC column, which indicates how well the model is able to discriminate between classes. The higher the AUC, the better the model is at predicting '0' classes as '0' and '1' classes as '1'. Similarly, the higher the AUC, the better the model is at discriminating between toxic and non-toxic messages. This can also be seen in the comparison graph in Figure 4, where it is clear that all models have a similar ROC curve. LSA with SVM is the ML combination that comes closest to BERTweet. This suggests to us a possible future work: it is worth testing LSA with a higher number of components (e.g., 10,000) and an SVC model with a C-parameter greater than 4 in order to optimise its global performance. At the same time, the slight difference in terms of performance between the simpler Logistic Regression model and the more complex Transformer model could be compensated by the model explainability. For this purpose, it might be helpful to look at Table 7, which shows the most significant words and their corresponding LR coefficients. The fact that BERTweet achieved a slightly better result is probably due to its ability to understand context, unlike other ML models. However, it is also true that our dataset collected individual tweets rather than whole dialogues, so this fact could minimise the potential of the Transformer and cause the model to focus mainly on significant words, reducing the performance gap between ML models. For this reason, another interesting future work is to re-train BERTweet on the same dataset but with these words masked, forcing it to try to find relationships and contexts of toxicity beyond the terms used. Another interesting aspect is the versatility and ambiguity of natural language, which offers many ways of writing offensive comments without necessarily using swearwords, but, for example, by referring to an external fact or assumption that an algorithm cannot directly extrapolate from the text but that a human can. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **Dataset** & **Emojis** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline \multirow{2}{*}{Fine Tuning} & \(1\) & No & 0.9238 & 0.9140 & 0.9084 & 0.9197 \\  & \(1\) & Yes & 0.9202 & 0.9108 & 0.8963 & 0.9259 \\ \hline \multirow{2}{*}{NN} & \(1\) & No & 0.9241 & 0.9136 & 0.9158 & 0.9115 \\  & \(1\) & Yes & 0.9235 & 0.9133 & 0.9117 & 0.9150 \\ \hline \multirow{2}{*}{NN freezing BERTweet} & \multirow{2}{*}{1} & \multirow{2}{*}{No} & \multirow{2}{*}{0.8326} & \multirow{2}{*}{0.7999} & \multirow{2}{*}{0.8444} & \multirow{2}{*}{0.7600} \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 4: BERTweet summary results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Cleaning** & **Model** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline \multirow{2}{*}{1} & [PERSON] & [PERSON] & 0.9190 & 0.9046 & 0.9391 & 0.8726 \\  & [PERSON] & [PERSON] & 0.8967 & 0.8788 & 0.9092 & 0.8504 \\  & [PERSON] & [PERSON] & 0.9155 & 0.8987 & 0.9376 & 0.8628 \\  & [PERSON] & [PERSON] & 0.9144 & 0.8973 & 0.9367 & 0.8611 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LDA with local models global results. At the same time, being able to rely on a dataset that includes entire conversations allows us to extrapolate contextual information about the sentiment of each message, monitor the conversation and detect unruly toxic comments that correspond to sudden changes in conversation sentiment.