Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia

Authors

  • Tigor Nirman Simanjuntak Directorate of Analysis and Development of Statistics, BPS Statistics Indonesia
  • Setia Pramana Politeknik Statistika STIS, Jakarta Indonesia

DOI:

https://doi.org/10.29244/ijsa.v5i2p304-313

Keywords:

covid-19 impact, tweets, sentiment analysis, supervised machine learning, vader

Abstract

This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.

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References

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Published

2021-06-30

How to Cite

Simanjuntak, T. N. ., & Pramana, S. . (2021). Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia. Indonesian Journal of Statistics and Its Applications, 5(2), 304–313. https://doi.org/10.29244/ijsa.v5i2p304-313

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Articles