International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 11 Issue IV Apr 2023- Available at www.ijraset.com

VII. CONCLUSION

In conclusion, cyberbullying is a serious issue that affects many individuals on social media platforms, and the detection of cyberbullying is essential to prevent its harmful effects. In this paper, we presented a proposed solution forcyberbullying detection with text, audio, andemoji data using a CR* model. We discussed the limitations and issues of earlier systems, includinglimited modality support, limited dataset size, lackof multimodal fusion, and limited audio and emojirepresentation. Our proposed system overcomesthese limitations and provides a more comprehensive and achieved average of 92.15% accuracy, 81.7% Precision, 93.8% Recall and 87.3% F1-score.

Future work can explore increasing the size and diversity of the dataset to improve the model's ability to detect rare, complex, and multilingual cyberbullying events. Overall, we believe our proposed system can make a valuable contribution to the field of cyberbullying detectionand help promote a safer and more inclusive socialmedia environment.

VIII. ACKNOWLEDGEMENT

We are deeply indebted to Dr.M.S.Anbarasi, Assistant Professor, Department of Information Technology, PuducherryTechnological University,Puducherry, India.

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