Cargando…
Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media
Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding minds...
Autor principal: | Stella, Massimo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924458/ https://www.ncbi.nlm.nih.gov/pubmed/33816946 http://dx.doi.org/10.7717/peerj-cs.295 |
Ejemplares similares
-
Event classification from the Urdu language text on social media
por: Awan, Malik Daler Ali, et al.
Publicado: (2021) -
Forma mentis networks quantify crucial differences in STEM perception between students and experts
por: Stella, Massimo, et al.
Publicado: (2019) -
Online Brand Community User Segments: A Text Mining Approach
por: Ge, Ruichen, et al.
Publicado: (2022) -
Improving text mining in plant health domain with GAN and/or pre-trained language model
por: Jiang, Shufan, et al.
Publicado: (2023) -
Introducing DynaPTI–constructing a dynamic patent technology indicator using text mining and machine learning
por: Freunek, Michael, et al.
Publicado: (2023)