CYBER BULLYING SENTIMENT ANALYSIS BASED ON SOCIAL CATEGORIES USING THE CHI-SQUARE TEST

Authors

  • Zulpan Hadi Universitas Teknologi Mataram
  • Emi Suryadi Universitas Teknologi Mataram
  • Ardiyallah Akbar Universitas Teknologi Mataram
  • Zaenudin Universitas Teknologi Mataram
  • Rudi Muslim Universitas Teknologi Mataram

DOI:

https://doi.org/10.69916/comtechno.v2i1.144

Keywords:

sentiment analysis, cyber bullying, chi-square, bag of words, classification

Abstract

This research evaluates various machine learning models in classifying sentiment in cyberbullying data across six categories: not_cyberbullying, gender, religion, other_cyberbullying, age, and ethnicity. Using a Bag of Words approach combined with Chi-Square feature selection (1000 features), models tested include SVM, Logistic Regression, Naïve Bayes, KNN, and Random Forest. Results show SVM and Logistic Regression achieving the highest accuracy at 83%, indicating their effectiveness in prediction. Naïve Bayes performed the poorest with 62% accuracy, suggesting a mismatch with the data or need for further tuning. KNN and Random Forest showed good performance with 75% and 81% accuracy respectively, though not as high as SVM and Logistic Regression. This multi-algorithm approach provides insights into each model's effectiveness and behavior on diverse data characteristics, essential for understanding the unique nuances of each cyberbullying category. Model selection should consider accuracy, interpretability, computational cost, and suitability to specific problem characteristics. This research aims to deepen understanding of cyberbullying to support more effective mitigation strategies.

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2024-07-30

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Hadi, Z., Suryadi, E., Akbar, A., Zaenudin, & Muslim, R. (2024). CYBER BULLYING SENTIMENT ANALYSIS BASED ON SOCIAL CATEGORIES USING THE CHI-SQUARE TEST. Journal Computer and Technology, 2(1), 01–09. https://doi.org/10.69916/comtechno.v2i1.144

Issue

Vol. 2 No. 1 (2024): Juli 2024

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Articles

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Copyright (c) 2024 Zulpan Hadi, Emi Suryadi, Ardiyallah Akbar, Zaenudin, Rudi Muslim

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