## 5 Conclusions  
The HSAN model is proposed in this paper for the automatic detection of the severity of cyberbullying incidents in online dialogues on social media and the classification of cyberbullying severity. The paper has created a novel dataset for detecting that includes three levels for the severity of cyberbullying incidents, and it helped the social worker to collect their attention on cyberbullying cases. Our proposed HSAN model also has improved classification in the three-level cyberbullying problem. In addition, the proposed HSAN model can provide researchers to implement classification detection in a format of multiple sentences in threaded discussion. The results of the current study indicate that \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**HAN**} & \multicolumn{4}{c}{**HSAN**} \\ \cline{2-9}  & _ACC_ & _PRE_ & _REC_ & \(F1_{overall}\) & _ACC_ & _PRE_ & _REC_ & \(F1_{overall}\) \\ \hline IMDB Review & 65.23\% & 65.47\% & 65.35\% & 65.09\% & 66.54\% & 66.60\% & 66.54\% & 66.57\% \\ Yahoo Answer & 65.98\% & 65.87\% & 65.92\% & 65.72\% & 66.03\% & 65.84\% & 66.03\% & 65.93\% \\ Yelp Review Polarity & 83.60\% & 83.77\% & 83.68\% & 83.57\% & 84.92\% & 85.03\% & 84.92\% & 84.97\% \\ Amazon Review Polarity & 70.83\% & 74.53\% & 72.63\% & 73.57\% & 77.65\% & 78.62\% & 77.65\% & 78.13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance on four public datasets with the English language. compared with the soft-attention mechanism, the SAM provided superior attention weight estimation results. In addition, implementing hierarchical networks for sentence- and word-level classification is suitable for hidden feature extraction and prediction in cyberbullying dialogues. For precisely estimating attention weight, the HSAN model applied higher attention weight to cyberbullying sentences than the HAN model did. Therefore, the HSAN model exhibited superior performance to the HAN model in the cyberbullying severity classification task. The three-level HSAN model had an \(F1_{overall}\) value of 58.94% and outperformed the other eight prediction models. Furthermore, the experimental data was unbalanced, which resulted in the training set data having the highest number of \"serious\" severity labels; however, the number of samples in validation and test sets had the lowest proportions, which resulted in relatively poor model prediction for the \"serious\" category. Cyberbullying may occur in the form of pictures or videos; however, the current study did not account for these two factors, because text represents most cyberbullying cases on social media platforms. In the future, the word embeddings in the HSAN model can be replaced with a pretrained Bidirectional Encoder Representations from Transformers (BERT)-based model. Since the BERT model has the restriction of sentence length, BERT could not obtain the effective features from the long length content, because the truncation approach is used to a fixed sentence length which is 512 tokens. However, the pretrained BERT model was formed with a considerable amount of textual data, and it is possible to get more precise semantic information. Therefore, the pretrained BERT model is used to replace the word encoder in the proposed model, because the maximum length (number of words) of a sentence in our experimental data is 28, so the pretrained BERT model may benefit the cyberbullying dataset. Moreover, a sentiment analysis can be used to extract additional sentimental features from text, because many negative words or sentences generated by a cyberbully may be used to present evidence of cyberbullying to someone other than the original victim. Conceptualization, [PERSON]-L.W.