Cyberbullying text detection in social media based on a deep learning model

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dc.contributor.advisorIspány, Márton
dc.contributor.authorYang, Lan
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2020-05-11T11:50:09Z
dc.date.available2020-05-11T11:50:09Z
dc.date.created2020-05-11
dc.description.abstractCyberbullying in social media is becoming more and more common because of the rapid development in the communication domain. The close connection between people brings the bullying manner from the physical world into cyberspace and does harm to the network environment. This thesis aims to utilize deep learning techniques to detect cyberbullying text. Based on two open-source datasets, we apply the pre-trained GloVe model to obtain the word embedding. We employ the RCNN model to classify the cyberbullying text with TensorFlow. During training, this thesis compares the different performance under different model configs. We show the process of optimization and get a well trained model. At last, we make a conclusion regarding the work we have done. Besides, we propose several directions for future work on coping cyberbullying manner.hu_HU
dc.description.courseComputer sciencehu_HU
dc.description.degreeegységes, osztatlanhu_HU
dc.format.extent49hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/286734
dc.language.isoenhu_HU
dc.subjectcyberbullyinghu_HU
dc.subjectdeep learninghu_HU
dc.subjecttext mininghu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titleCyberbullying text detection in social media based on a deep learning modelhu_HU
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