IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies.

Mu, Guangyu; Liao, Zehan; Li, Jiaxue; Qin, Nini; Yang, Ziye

Mu, Guangyu; Liao, Zehan; Li, Jiaxue; Qin, Nini; Yang, Ziye.

Afiliación

Mu G; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
Liao Z; Key Laboratory of Financial Technology of Jilin Province, Changchun, China.
Li J; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
Qin N; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.
Yang Z; School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China.

PLoS One ; 18(10): e0292677, 2023.

Article en En | MEDLINE | ID: mdl-37815983

Asunto(s)

Urgencias Médicas; Opinión Pública; Humanos; Algoritmos; Procesos de Grupo; Internet

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Opinión Pública / Urgencias Médicas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google