<abstract xmlns="http://www.w3.org/1999/xhtml"><p>From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.</p></abstract>

From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically engaging on Twitter to convey their sentiments. As such, this paper aims to identify the public opinion of Filipino twitter users concerning COVID-19 in three different timelines. Toward this goal, a total of 65,396 tweets related to COVID-19 were sent to data analysis using R Statistical Software. Results show that “mask”, “health”, “lockdown”, “outbreak”, “test”, “kit”, “university”, “alcohol”, and “suspension” were some of the most frequently occurring words in the tweets. The study further investigates Filipinos’ emotions regarding COVID-19 by calculating text polarity of the dataset. To date, this is the first paper to perform sentiment analysis on tweets pertaining to COVID-19 not only in the Filipino context but worldwide as well.

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

From the outbreak of a novel COronaVIrus Disease (COVID-19) in Wuhan to the first COVID-19 case in the Philippines, Filipinos have been enthusiastically...

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Data publikacji: 10 gru 2020

Zakres stron: 141 - 155

Otrzymano: 17 mar 2020

Przyjęty: 07 paź 2020

DOI: https://doi.org/10.2478/cait-2020-0052

Słowa kluczowe
Sentiment analysis, coronavirus disease, Philippines, Twitter, opinion mining, COVID-19, NCOV, virus, tweets, Data Mining

© 2020 Manuel B. Garcia, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines

Data publikacji: 10 gru 2020

Zakres stron: 141 - 155

Otrzymano: 17 mar 2020

Przyjęty: 07 paź 2020

DOI: https://doi.org/10.2478/cait-2020-0052

Słowa kluczoweSentiment analysis, coronavirus disease, Philippines, Twitter, opinion mining, COVID-19, NCOV, virus, tweets, Data Mining

© 2020 Manuel B. Garcia, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Sentiment analysis, coronavirus disease, Philippines, Twitter, opinion mining, COVID-19, NCOV, virus, tweets, Data Mining