Stacking Ensemble Learning Technique for Sentiment Analysis of Hate Speech on Twitter
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Abstract
Hate speech on social media, especially on the Twitter platform, is an increasing problem. The spread of negative, demeaning, or threatening messages can have a negative impact on individuals and society at large. Previous research focuses more on improving accuracy in general without looking at the f1-score on tweets categorized as negative. Therefore, this research aims to address these issues by improving the accuracy and f1-score of the overall sentiment analysis of hate speech on Twitter. Through the use Stacking Ensemble Learning technique, It was found that the combination of models consisting of Support Vector Machine (SVM), Decision Tree, and Random Forest was able to provide the most accurate results with an accuracy rate of 96.16% and F1 Score of 96.13%. This research demonstrates the potential of using these techniques to effectively and efficiently identify and address hate speech on Twitter, and contributes to creating a more positive and safe online environment.
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