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引用次数: 0
摘要
在这个数字化的时代,人们越来越倾向于技术。这既有积极的一面,也有消极的一面,其中一个消极的影响是网络欺凌。网络欺凌允许人们在各种社交媒体平台上发表负面评论,该项目旨在检测和分类此类文本。利用BiLSTM (Bidirectional长短期记忆)和BERT(Bidirectional Encoder Representations from Transformers)对各种社交媒体平台上的网络欺凌文本进行识别,并将其分为宗教、年龄、性别、种族、not_cyberbullying和other_cyberbullying等类别。这有助于记录社交媒体上发生的网络欺凌类型,以及在举报并采取严格措施后,这种情况是否会随着时间的推移而减少。
Identification and Labeling of Textual Cyberbullying using BiLSTM and BERT
In this aeon of digitalization, people are inclining more and more towards technology. This has both positive and negative aspects, and one of the negative effects is cyberbullying. Cyberbullying allows people to comment negatively on various social media platforms, this project aims at detecting and classifying such text. Using BiLSTM (Bidirectional Long Shortterm Memory) and BERT(Bidirectional Encoder Representations from Transformers), the cyberbullying text on various social media platforms is identified and classified into classes such as religion, age, gender, ethnicity, not_cyberbullying and other_cyberbullying. This helps in keep the record of types of cyberbullying that occurs on social media and whether it’s reduced over time after reporting it and taking strict measures against it.