ArticleProcedia computer science2022
Public sentiment towards face-to-face activities during the COVID-19 pandemic in Indonesia.
Article in Procedia computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A year after the COVID-19 pandemic took place, activities that were carried out online gradually switched back to face-to-face. This has caused controversy given the high transmission. Therefore, this study aims to analyze public sentiment by utilizing Twitter data. Latent Dirichlet Allocation (LDA) was also conducted in this study to classify public opinion. It was found that face-to-face learning was the highlight of public conversation and was dominated by negative sentiment, followed by neutral and positive sentiment. Meanwhile, the LDA model produced topics about vaccination, public preference, school reopening, public sentiment, students' longing for face-to-face learning and face-to-face learning plan.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.