Analysis of the Influence of Emotions on Hate Speech on Twitter: A Study Using SVM and Correlation Methods
Analisis Pengaruh Emosi terhadap Hate Speech di Twitter: Studi dengan Metode SVM dan Korelasi
DOI:
https://doi.org/10.21070/ups.5226Keywords:
Classification, Correlation analysis, Hate Speech, Emotions, Support Vector Machine, TwitterAbstract
In the digital era, social media platforms like Twitter play a crucial role in Indonesian society but also facilitate the spread of hate speech. This study examines the influence of emotions (Anticipation, Trust, Joy, Anger, Disgust, Fear, Sadness, and Surprise) on hate speech propagation on Twitter using Support Vector Machine (SVM) and correlation analysis. The dataset was split 80% for training and 20% for testing, involving manual labeling, text preprocessing, TF-IDF word weighting, and SVM classification. Test results showed an accuracy of 87%, precision of 83%, recall of 26%, and an f1-score of 30%. The web application classifies tweets as hate speech or non-hate speech and identifies emotions in tweets. Correlation analysis revealed a moderate positive correlation between hate speech and Anger (0.34) and Disgust (0.35), and a negative correlation with Anticipation (-0.37). This study provides valuable insights into the emotional dynamics of hate speech on Twitter.
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