论文标题
使用MNB和SVM分类器检测Bangla假新闻
Detection of Bangla Fake News using MNB and SVM Classifier
论文作者
论文摘要
由于许多商业和政治原因，假新闻已经大量出现了，并且在在线世界中变得频繁。这些假新闻的捏造词对离线社区产生了巨大影响，人们可以轻松地被这些假新闻污染。因此，对这一领域的研究兴趣已经上升。关于从英语文本和其他语言中发现的假新闻的检测，但一些孟加拉语言进行了重大研究。我们的工作反映了有关从社交媒体中发现孟加拉假新闻的实验分析，因为该领域仍然需要很多重点。在这项研究工作中，我们使用了两种有监督的机器学习算法，多项式幼稚贝叶斯（MNB）和支持向量机（SVM）分类器来检测具有Count -count vectorizer和术语频率的Bangla Fake News -underse频率 - 逆文档频率vectorizer作为特征提取。我们提出的框架根据相应文章的极性检测到假新闻。最后，我们的分析显示了与线性内核的SVM，精度为96.64％的表现均优于MNB，精度为93.32％。
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%.
