基于MNB和SVM分类器的孟加拉假新闻检测
Detection of Bangla Fake News using MNB and SVM Classifier
摘要：
由于许多商业和政治原因，假新闻已经大量出现，并且在网络世界中变得频繁。人们很容易受到这些假新闻的污染，因为它们的捏造言论对离线社区产生了巨大的影响。因此，对这一领域的研究兴趣有所上升。在检测英语文本和其他语言的假新闻方面进行了大量研究，但孟加拉语的研究很少。我们的工作反映了对社交媒体中孟加拉假新闻检测的实验分析，因为这一领域仍然需要更多的关注。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，多项朴素贝叶斯（MNB）和支持向量机（SVM）分类器，以CountVectorizer和词频-逆文档频率Vectorizer作为特征提取来检测孟加拉假新闻。我们提出的框架根据相应文章的极性来检测假新闻。最后，我们的分析表明，支持向量机与线性核的准确率为96.64%优于MNB的准确率为93.32%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
