Detection of Hoax News Using TF-IDF Vectorizer and Multinomial Naïve Bayes and Passive Aggressive
DOI:
https://doi.org/10.62205/mjgcs.v1i2.24Keywords:
Hoax, New Website, Tf-idf Vectorizer, Naive Bayes Multinominal, Passive AggressiveAbstract
The website is a source of information, but not all information is guaranteed to be correct. Some news can be
considered hoaxes or not based on facts. This research aims to build a hoax news detection system on English language
news websites. The method used involves the multinomial Naive Bayes and Passive Aggressive approaches.
Classification report analysis shows the superiority of the Passive Aggressive Classifier with significant improvements
in all evaluation metrics compared to Multinomial Naïve Bayes. The conclusion is based on the characteristics of the
dataset, confirming the effectiveness of the Passive Aggressive Classifier in solving the task of classifying fake news in
English, with the highest accuracy reaching 93.74%.
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