SUPPORT VECTOR MACHINE IMPLEMENTATION FOR RACIAL CYBERBULLYING SENTIMENT ANALYSIS IN YOUTUBE COMMENTS
This study focuses on developing a model for detecting and classifying cyberbullying, particularly related to hate speech based on ethnicity, religion, race, and intergroup relations, using Support Vector Machine (SVM) and TF-IDF weighting. Data was collected from YouTube comments and categorized...
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| Main Author: | |
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/86873 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | This study focuses on developing a model for detecting and classifying
cyberbullying, particularly related to hate speech based on ethnicity, religion, race,
and intergroup relations, using Support Vector Machine (SVM) and TF-IDF
weighting. Data was collected from YouTube comments and categorized into two
classes: non-cyberbullying and Racial-based cyberbullying. The model was
designed to classify negative sentiment comments containing cyberbullying. A
confusion matrix evaluation was applied to assess model accuracy, precision, and
recall, using k-fold cross-validation. Results showed varying model performance
across each fold, with an overall average accuracy of 85%, F1-score of 89%, and
recall of 90%, with the best performance observed in 3
rd folds and 4th. The model
struggled to detect Racial-based cyberbullying in fold 5th, affecting its overall
performance. This study demonstrates that SVM is fairly effective in detecting
cyberbullying, though improvements are needed to enhance classification accuracy
and ensure that various forms and categories of cyberbullying are adequately
identified.
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