Using K-Nearest Neighbor to Classify Hate Speech and Emotions on Twitter
Penggunaan K-Nearest Neighbor untuk Klasifikasi Hate Speech dan Emosi pada Twitter
DOI:
https://doi.org/10.21070/ups.7317Keywords:
Clasification, Hate Speech, Emotion, K-Nearest Neighbor, TwitterAbstract
Hate speech is an expression that has a purpose against an individual or group. The expression can be in the form of inciting and spreading slander, justifying, or encouraging hatred, even discrimination and violence based on various reasons. This study focuses on the Twitter platform using the k-nearest neighbor method. In this study, the dataset used was 1842 "non-hate speech" data and 2130 "hate speech," which was divided into 80% training data and 20% test data. The results of the evaluation of the test data using the confusion matrix showed an average accuracy value for hate speech classification of 0.855, while for emotion classification it was 0.534. Based on these results, it can be concluded that the k-nearest neighbor algorithm is quite effective in analyzing and classifying hate speech and emotions in Twitter text with certain datasets.
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