Detection of Bangla Fake News using MNB and SVM Classifier
- URL: http://arxiv.org/abs/2005.14627v1
- Date: Fri, 29 May 2020 15:38:54 GMT
- Title: Detection of Bangla Fake News using MNB and SVM Classifier
- Authors: Md Gulzar Hussain, Md Rashidul Hasan, Mahmuda Rahman, Joy Protim, and
Sakib Al Hasan
- Abstract summary: This research work has been conducted on the detection of fake news from English texts and other languages but a few in Bangla Language.
In this work, we have used two supervised machine learning algorithms, Multinomial Naive Bayes and Support Machine (SVM)
Our proposed framework detects fake news with accuracy of 96.64% depending on the polarity of the corresponding article.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Fake news has been coming into sight in significant numbers for numerous
business and political reasons and has become frequent in the online world.
People can get contaminated easily by these fake news for its fabricated words
which have enormous effects on the offline community. Thus, interest in
research in this area has risen. Significant research has been conducted on the
detection of fake news from English texts and other languages but a few in
Bangla Language. Our work reflects the experimental analysis on the detection
of Bangla fake news from social media as this field still requires much focus.
In this research work, we have used two supervised machine learning algorithms,
Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM) classifiers to
detect Bangla fake news with CountVectorizer and Term Frequency - Inverse
Document Frequency Vectorizer as feature extraction. Our proposed framework
detects fake news depending on the polarity of the corresponding article.
Finally, our analysis shows SVM with the linear kernel with an accuracy of
96.64% outperform MNB with an accuracy of 93.32%.
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