报告详情
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
所在会场：[暂无会议] [暂无会议段]
暂无文件
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
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
-
12月30日 2025
报告提交截止日期
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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
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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