Mining Hidden Topics from Newspaper Quotations: The COVID-19 Pandemic

In this paper, we extract quotations from Al Jazeera’s news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to se...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Tạ, Hoàng Thắng
التنسيق: Conference paper
اللغة:English
منشور في: 2023
الوصول للمادة أونلاين:http://scholar.dlu.edu.vn/handle/123456789/2006
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
Thư viện lưu trữ: Thư viện Trường Đại học Đà Lạt
الوصف
الملخص:In this paper, we extract quotations from Al Jazeera’s news articles containing keywords related to the COVID-19 pandemic. We apply Latent Dirichlet allocation (LDA), coherence measures, and clustering algorithms to unsupervisedly explore latent topics from the dataset of about 3400 quotations to see how coronavirus impacts human beings. By combining noun phrases as inputs before the training and Cv measure for coherence values, we obtain an average coherence value of 0.66 with a least average number of topics of 24.8. The result covers some of the top issues that our world has been facing against the COVID-19 pandemic.