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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Kristien Margi Suryaningrum
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Comparison of the TF-IDF Method with the Count Vectorizer to Classify Hate Speech Kristien Margi Suryaningrum
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 2 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i2.9978

Abstract

Hate speech is a form of expression used to spread hatred and commit acts of violence and discrimination against a person or group of people for various reasons. Cases of hate speech are very common in social media, one of which is Twitter. The goal to be achieved is to create a system that can classify a tweet on Twitter into hate speech (HS) or non-hate speech (NONHS) classes. The method used is Support Vector Machine by comparing the features of TF-IDF and Count Vectorizer. And the parameters compared are seen from accuracy, precision, recall, and f1-score. Results obtained, overall, by using the TF-IDF feature, the Support Vector Machine algorithm gets high results compared to the Count Vectorizer feature, with an accuracy value of 88.77%, 87.45% precision, 88.77% recall, and f1-score of 87.81%.
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