International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Slangs and Short forms of Malay Twitter Sentiment Analysis using Supervised Machine Learning

  • Yin, Cheng Jet (Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM)) ;
  • Ayop, Zakiah (Information Security Forensics and Computer Networking (INSFORNET)) ;
  • Anawar, Syarulnaziah (Information Security Forensics and Computer Networking (INSFORNET)) ;
  • Othman, Nur Fadzilah (Information Security Forensics and Computer Networking (INSFORNET)) ;
  • Zainudin, Norulzahrah Mohd (Jabatan Sains Komputer, Fakulti Sains dan Teknologi Pertahanan, Universiti Pertahanan Nasional Malaysia (UPNM))
  • 투고 : 2021.11.05
  • 발행 : 2021.11.30
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초록

The current society relies upon social media on an everyday basis, which contributes to finding which of the following supervised machine learning algorithms used in sentiment analysis have higher accuracy in detecting Malay internet slang and short forms which can be offensive to a person. This paper is to determine which of the algorithms chosen in supervised machine learning with higher accuracy in detecting internet slang and short forms. To analyze the results of the supervised machine learning classifiers, we have chosen two types of datasets, one is political topic-based, and another same set but is mixed with 50 tweets per targeted keyword. The datasets are then manually labelled positive and negative, before separating the 275 tweets into training and testing sets. Naïve Bayes and Random Forest classifiers are then analyzed and evaluated from their performances. Our experiment results show that Random Forest is a better classifier compared to Naïve Bayes.

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과제정보

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

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