Hate Speech Detection Using Support Vector Machine (SVM) Method
Deteksi Ujaran Kebencian Menggunakan Metode Support Vector Machine (SVM)
DOI:
https://doi.org/10.21070/ups.2545Keywords:
Predictions, Hate Speech, SVM, XGBoost, RSCVAbstract
Hate speech is a linguistic phenomenon that deviates from the norms and polite grammar in language and communication ethics. This research is aimed at detecting a word or sentence containing or not containing a hate speech using the SVM method for classification. This research takes data using the Tweepy API and gets a total sample data of 1681. To do word weighting, researchers use TF-IDF to find out the frequency of words that often arise in the dataset. In the classification process, researchers used two methods, namely SVM and XGBoost which then from the best results in SVM with 90% training data and 10% test data obtained a training score of 95.87% and a test score of 87.30% with a gap of 8.57% then from the SVM method was tuned using RSCV and managed to increase the training score by 100% test score of 93.20% with a gap of 6.80%.
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