Asset Details

MbrlCatalogueTitleDetail

Do you wish to reserve the book?

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

by Chen, Junxiang , Zhu, Tingshao , Chen, Chen , Zheng, Chengda , Xue, Jia , Li, Sijia

in Application programming interface / Betacoronavirus / Biology and Life Sciences / Canada / Computer and Information Sciences / Coronaviridae / Coronavirus Infections - psychology / Coronaviruses / COVID-19 / Data analysis / Data collection / Data Collection - methods / Data mining / Datasets / Diamonds / Dirichlet problem / Economic impact / Emotions / Epidemics / Fear - psychology / Humans / Impact analysis / Information Dissemination / Interpersonal relations / Learning algorithms / Machine Learning / Medicine and Health Sciences / Natural language processing / Online social networks / Outbreaks / Pandemics / Pneumonia, Viral - psychology / Psychological aspects / Psychology / SARS-CoV-2 / Sentiment analysis / Social Media - classification / Social networks / Social Sciences / Supply chains / Tagging / Technology application / Viral diseases

2020

Yes Please

Hey, we have placed the reservation for you!

By the way, why not check out events that you can attend while you pick your title.

Oops! Something went wrong.

Looks like we were not able to place the reservation. Kindly try again later.

Are you sure you want to remove the book from the shelf?

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

by Chen, Junxiang , Zhu, Tingshao , Chen, Chen , Zheng, Chengda , Xue, Jia , Li, Sijia

2020

Confirm

Do you wish to request the book?

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

by Chen, Junxiang , Zhu, Tingshao , Chen, Chen , Zheng, Chengda , Xue, Jia , Li, Sijia

2020

Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy

How would you like to get it?

Submit

We have requested the book for you!

Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.

Oops! Something went wrong.

Looks like we were not able to place your request. Kindly try again later.

Journal Article

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

Chen, Junxiang,

Zhu, Tingshao,

Chen, Chen,

Zheng, Chengda,

Xue, Jia,

Li, Sijia

2020

Overview

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including \"updates about confirmed cases,\" \"COVID-19 related death,\" \"cases outside China (worldwide),\" \"COVID-19 outbreak in South Korea,\" \"early signs of the outbreak in New York,\" \"Diamond Princess cruise,\" \"economic impact,\" \"Preventive measures,\" \"authorities,\" and \"supply chain.\" Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

Share this book

Add to My Shelf

Publisher

Public Library of Science,Public Library of Science (PLoS)

Subject

Application programming interface

/ Betacoronavirus

/ Biology and Life Sciences

/ Canada

/ Computer and Information Sciences

/ Coronaviridae

/ Coronavirus Infections - psychology