A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.

Rustam, Furqan; Khalid, Madiha; Aslam, Waqar; Rupapara, Vaibhav; Mehmood, Arif; Choi, Gyu Sang

Rustam, Furqan; Khalid, Madiha; Aslam, Waqar; Rupapara, Vaibhav; Mehmood, Arif; Choi, Gyu Sang.

Afiliação

Rustam F; Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
Khalid M; Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
Aslam W; Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
Rupapara V; School of Computing and Information Sciences Florida International University, Miami, FL, United States of America.
Mehmood A; Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan.
Choi GS; Department of Information & Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Korea.

PLoS One ; 16(2): e0245909, 2021.

Article em En | MEDLINE | ID: mdl-33630869

Assuntos

COVID-19; Mídias Sociais; Aprendizado de Máquina Supervisionado; Aprendizado Profundo; Humanos; Processamento de Linguagem Natural; Pandemias; Opinião Pública

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Contexto em Saúde: 2_ODS3 Base de dados: MEDLINE Assunto principal: Mídias Sociais / Aprendizado de Máquina Supervisionado / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: PLoS One Ano de publicação: 2021 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google