Research on the evolution of public opinion and topic recognition based on multi-source data mining Online publication date: Mon, 19-Dec-2022
by Zeguo Qiu; Baiyan He
International Journal of Computer Applications in Technology (IJCAT), Vol. 69, No. 3, 2022
Abstract: In the era of rapid network development, the internet has become the main medium for the spread of online public opinion. Users can express their views on hot issues through text, pictures, and videos anytime and anywhere, analyse the evolving trend of netizens' emotions in emergencies to discover the law of public opinion evolution and potential risks, and provide decision support for public opinion guidance and control. In this paper, Python is used to pre-process the collected text data, and proposes a method based on spectral clustering algorithm, using the Latent Dirichlet Allocation to extract text topics. From the perspective of multi-language and multi-source data, it mines high-value public opinion topics in online public opinion, and uses data visualisation methods to study the emotional tendencies of netizens. The research results can clearly reveal the content of discussion and the emotional attitude of netizens in the period of public opinion transmission.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook