Capturing and analyzing social representations: a first application of Natural Language Processing techniques to reader’s comments in COVID-19 news : Argentina, 2020
We present a first approximation to the quantification of social representations about the COVID-19, using news comments. A web crawler was developed to construct the dataset of reader’s comments. We detect relevant topics in the dataset using Latent Dirichlet Allocation, and analyze its evolution d...
Guardado en:
| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/114634 http://49jaiio.sadio.org.ar/pdfs/agranda/AGRANDA-02.pdf |
| Aporte de: |
| Sumario: | We present a first approximation to the quantification of social representations about the COVID-19, using news comments. A web crawler was developed to construct the dataset of reader’s comments. We detect relevant topics in the dataset using Latent Dirichlet Allocation, and analyze its evolution during time. Finally, we show a first prototype to the prediction of the majority topics, using FastText. |
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