International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Cyberbullying Detection in Twitter Using Sentiment Analysis

  • Theng, Chong Poh (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka) ;
  • Othman, Nur Fadzilah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka) ;
  • Abdullah, Raihana Syahirah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka) ;
  • Anawar, Syarulnaziah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka) ;
  • Ayop, Zakiah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka) ;
  • Ramli, Sofia Najwa (Faculty of Science and Information Technology Universiti Tun Hussien Onn Malaysia)
  • 투고 : 2021.11.05
  • 발행 : 2021.11.30
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초록

Cyberbullying has become a severe issue and brought a powerful impact on the cyber world. Due to the low cost and fast spreading of news, social media has become a tool that helps spread insult, offensive, and hate messages or opinions in a community. Detecting cyberbullying from social media is an intriguing research topic because it is vital for law enforcement agencies to witness how social media broadcast hate messages. Twitter is one of the famous social media and a platform for users to tell stories, give views, express feelings, and even spread news, whether true or false. Hence, it becomes an excellent resource for sentiment analysis. This paper aims to detect cyberbully threats based on Naïve Bayes, support vector machine (SVM), and k-nearest neighbour (k-NN) classifier model. Sentiment analysis will be applied based on people's opinions on social media and distribute polarity to them as positive, neutral, or negative. The accuracy for each classifier will be evaluated.

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과제정보

This publication has been supported by Center of Research and Innovation Management (CRIM), Universiti Teknikal Malaysia Melaka (UTeM). The authors would like to thank UTeM and INSFORNET research group members for their supports.

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