Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia
DOI:
https://doi.org/10.29244/ijsa.v5i2p304-313Keywords:
covid-19 impact, tweets, sentiment analysis, supervised machine learning, vaderAbstract
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.
Downloads
References
Alhajji, M., Al Khalifah, A., Aljubran, M., & Alkhalifah, M. (2020). Sentiment analysis of tweets in Saudi Arabia regarding governmental preventive measures to contain COVID-19.
Asri, A. S., & Mariyah, S. (2019). Subjective Happiness Index based on Twitter in Indonesia. Retrieved from https://communities.unescap.org/asia-pacific-economic-statistics/apes-2019-featured-papers
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1): 1–8.
Dubey, A. D. (2020). Twitter sentiment analysis during COVID19 outbreak. Available at SSRN 3572023.
Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR), 49(2): 1–41.
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(12): 2009.
Hippner, H., & Rentzmann, R. (2006). Text mining. Informatik-Spektrum, 29(4): 287–290.
Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).
Kaur, C., & Sharma, A. (2020). Twitter Sentiment Analysis on Coronavirus using Textblob. EasyChair.
Medford, R. J., Saleh, S. N., Sumarsono, A., Perl, T. M., & Lehmann, C. U. (2020). An “infodemicâ€: leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak. Open Forum Infectious Diseases, 7(7), ofaa258. Oxford University Press US.
Mohammad, S. (2012). # Emotional tweets. * SEM 2012: The First Joint Conference on Lexical and Computational Semantics–Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), 246–255.
Reyes, A., Rosso, P., & Veale, T. (2013). A multidimensional approach for detecting irony in twitter. Language Resources and Evaluation, 47(1): 239–268.
Virati, M. Q., Agustiyani, R., Mariyah, S., & Pramana, S. (2019). Development of a big data analysis system (Case Study: Unemployment statistics). Retrieved from https://communities.unescap.org/asia-pacific-economic-statistics/apes-2019-featured-papers
