Ensemble Machine Learning to Detect Sarcams in English on Twitter Social Media
Ensemble Machine Learning untuk Mendeteksi Sarkasme Dalam Bahasa Inggris pada Sosial Twitter
DOI:
https://doi.org/10.21070/ups.746Keywords:
Sarcasm, Sosial Media, Detection Sarcasme, Machine Learning, Sentiment AnalysisAbstract
Sarcasm on social media is often used to mock or hurt someone using language or words that appear positive but have a negative meaning. This poses a challenge in sentiment analysis on social media because it's difficult to detect sarcasm even for humans. The aim of this study is to compare the performance of four machine learning methods, namely Logistic Regression, Naive Bayes, Decision Tree, and Support Vector Machine, in detecting sarcastic sentences in news headlines. The dataset used contains news headlines in English. The results show that the Support Vector Machine method has the best performance with a model of 20 that has an accuracy score of 80% and an f1-score of 80%, compared to the Logistic Regression, Naive Bayes, and Decision Tree methods. Therefore, it can be concluded that the Support Vector Machine method is the best solution for detecting sarcasm.
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