Leveraging Text Sentiment Analysis for Cyberbullying Prevention
编号:78 访问权限:仅限参会人 更新:2025-12-21 13:00:39 浏览:22次 拓展类型2

报告开始:暂无开始时间(Asia/Amman)

报告时间:暂无持续时间

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摘要
In today’s digital era, cyberbullying is a rising phenomenon with major effects on victims’ mental health and well-being. This Master’s thesis report investigates cyberbullying and presents a unique strategy to prevent it through the use of text sentiment analysis algorithms. The suggested Cyberbullying Prevention using Text Sentiment Analysis Algorithm compares the performance of three models: Convolutional Neural Network-Long Short Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Naive Bayes. The models were trained using a dataset of cyberbullying-related social media postings and communications. The results of the experiment show that the SVM model outperformed the other two models with an accuracy of 92% in detecting instances of cyberbullying. The CNN-LSTM model achieved an accuracy of 88%, while the Naive Bayes model achieved an accuracy of 83%. Social media businesses, schools, and other institutions can utilize the suggested method to detect and prevent cyberbullying in online communication. By detecting cyberbullying early on, steps may be taken to protect victims and foster a safer and better online environment. This study emphasizes the efficacy of utilizing text sentiment analysis algorithms to combat cyberbullying and provides useful insights into the performance of various models in identifying cyberbullying.
 
关键词
cyberbull,social media,machine learning,deep learning,classification,convolutional neural network,long short term memory,Natural Language Processing,sentiment analysis,twitter
报告人
Ali Rachini
Assistant Professor Holy Spirit University of Kaslik

稿件作者
Samir Haddad University of Balamand
Kassem Hamze Islamic University Of Lebanon
Ali Rachini Holy Spirit University of Kaslik
Joseph Merhej lebanese university
Jinane Sayah University of Balamand
Saeed El-Ghareeb university of the Basque country
Chadi Kallab Lebanese American University
Abbas Al-Jawahiry islamic university of lebanon
Bilal Alalawi islamic university of lebanon
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重要日期
  • 会议日期

    12月29日

    2025

    12月31日

    2025

  • 12月30日 2025

    报告提交截止日期

  • 12月30日 2025

    注册截止日期

  • 12月31日 2025

    初稿截稿日期

主办单位
国际科学联合会
承办单位
扎尔卡大学
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