Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set | Zendy

Adrien Boukobza | Zendy; Anita Burgun | Zendy; Bertrand Roudier | Zendy; Rosy Tsopra | Zendy

Open Access

Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set

Author(s) -

Adrien Boukobza,

Anita Burgun,

Bertrand Roudier,

Rosy Tsopra

Publication year - 2022

Publication title -

jmir medical informatics

Language(s) - English

Resource type - Journals

ISSN - 2291-9694

DOI - 10.2196/34306

Subject(s) - sentiment analysis , convolutional neural network , computer science , social media , set (abstract data type) , tag cloud , pandemic , cloud computing , deep learning , artificial intelligence , public opinion , representation (politics) , topic model , population , covid-19 , data science , natural language processing , world wide web , visualization , political science , medicine , disease , pathology , infectious disease (medical specialty) , programming language , environmental health , politics , law , operating system

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research