Sentiment and Mobility Analysis on COVID-19 Restrictions with Autoencoder-Reference-Cited by-同舟云学术

Sentiment and Mobility Analysis on COVID-19 Restrictions with Autoencoder

Published:2022-11-22 Issue: Volume: Page:
ISSN:
Container-title:7th International Conference on Sustainable Information Engineering and Technology 2022
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Author:

Sujarmanto Weldy¹^ORCID,Nalle Merpati Vortuna Amerta¹^ORCID,Maulida Nurul¹^ORCID,Putra Bayu Chandra¹^ORCID,Bere Stevania Hildegardis¹^ORCID,Yudistira Novanto¹^ORCID

Affiliation:

1. Brawijaya University, Indonesia)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3568231.3568251

Reference19 articles.

1. Community mobility in the European regions during COVID-19 pandemic: A partitioning around medoids with noise cluster based on space–time autoregressive models

2. Global Change Data Lab. 2022. Daily new confirmed COVID-19 cases per million people. https://ourworldindata.org/explorers/coronavirus-data-explorer Global Change Data Lab. 2022. Daily new confirmed COVID-19 cases per million people. https://ourworldindata.org/explorers/coronavirus-data-explorer

3. Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep Learning . MIT Press , Cambridge, Massachusetts . http://www.deeplearningbook.org. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press, Cambridge, Massachusetts. http://www.deeplearningbook.org.

4. Google. 2022. Google LLC ‘‘Google COVID-19 Community Mobility Reports”. https://www.google.com/covid19/mobility/ Google. 2022. Google LLC ‘‘Google COVID-19 Community Mobility Reports”. https://www.google.com/covid19/mobility/

5. Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe