Echoes of Crisis: A Sentiment Analysis of Sri Lanka’s Economic Tragedy
Abstract
Background: Sentiment analysis has become famous for extracting human emotions from text. The current study uses Twitter to show how people perceive Sri Lanka's economic woes. In the year 2022, Sri Lanka experienced a significant economic breakthrough.
Objective: The current study presents a novel viewpoint on how individuals responded to Sri Lanka's economic crisis in terms of sentiments. There can be particular information requirements because of the crisis. This study is essential for authorities to make decisions.
Method: The qualitative method was used to conduct the study with the help of the qualitative data analysis software NVivo. The researchers downloaded two sets of data regarding Sri Lanka’s economic crisis corresponding to two different periods using Twitter's hashtag #SriLankaEconomicCrisis. Then, NVivo processed and analysed both data sets to identify positive, negative, and neutral sentiments.
Findings: In the first dataset, which included 2687 tweets, 82.81% expressed negative sentiment, 11.31% expressed positive sentiment, and the remaining tweets expressed neutral sentiment. Meanwhile, with the second data set, out of 1,555 tweets, 98.59% of the tweets exhibit negative sentiment, 14.21% exhibit positive sentiment, and only 1.41% exhibit neutral sentiment.
Conclusion: The research indicates that the majority of the tweets in both datasets have negative opinions on Sri Lanka's economic problems, underscoring the depth of the public's dissatisfaction. The study also fills a significant vacuum in the literature and provides crucial information for decision-makers.
References
2. Abdukhamidov E, Juraev F, Abuhamad M, El-Sappagh S, AbuHmed T. Sentiment analysis of users’ reactions on social media during the pandemic. Electronics. 2022;11(10):1648.
3. Giachanou A, Crestani F. Like it or not: a survey of Twitter sentiment analysis methods. ACM Comput Surv. 2016 Jun 30;49(2):1-41.
4. Al Sari B, Alkhaldi R, Alsaffar D, Alkhaldi T, Almaymuni H, Alnaim N, Alghamdi N, Olatunji SO. Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms. J Big Data. 2022 Feb 18;9(1):21.
5. Shahbaznezhad H, Dolan R, Rashidirad M. The role of social media content format and platform in users’ engagement behavior. J Interact Mark. 2021 Feb;53(1):47-65.
6. Pohl D, Bouchachia A, Hellwagner H. Social media for crisis management: clustering approaches for sub-event detection. Multimed Tools Appl. 2015 Jun;74(11):3901-32.
7. Hughes AL, Palen L. Twitter adoption and use in mass convergence and emergency events. Int J Emerg Manag. 2009 Jan 1;6(3-4):248-60.
8. Babu NV, Kanaga EG. Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput Sci. 2022 Jan;3(1):74.
9. Dahish Z, Miah SJ. Exploring sentiment analysis research: a social media data perspective. Int J Soft Comput. 2023 Feb;14(1):1-12.
10. Oliveira DJ, Bermejo PH, Pereira JR, Barbosa DA. A aplicação da técnica de análise de sentimento em mídias sociais como instrumento para as práticas da gestão social em nível governamental. Rev Admin Púb. 2019 Jan;53(1):235-51. Portugese.
11. Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl Based Syst. 2015 Nov 1;89:14-46.
12. Ekanayake A, Amirthalingam K. The economic impact of the Covid-19 pandemic on Sri Lankan migrants in Qatar. Comp Migr Stud. 2021 Dec;9(1):38.
13. Sultana G. Economic crisis in Sri Lanka: an assessment. MP-IDSA Issue Brief; 2022. p. 1-11.
14. Bashir S, Bano S, Shueb S, Gul S, Mir AA, Ashraf R, Noor N. Twitter chirps for Syrian people: sentiment analysis of tweets related to Syria chemical attack. Int J Disaster Risk Reduct. 2021 Aug 1;62(Jun):102397.
15. Vijay T, Chawla A, Dhanka B, Karmakar P. Sentiment analysis on COVID-19 Twitter data. Proceedings of 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2020; 2020 Dec 1. p. 1-7.
16. Pandey D, Wairya S, Pradhan B. Understanding COVID-19 response by Twitter users: a text analysis approach. Heliyon. 2022 Aug 1;8(8):e09994.
17. Yaqub U. Tweeting during the Covid-19 pandemic: sentiment analysis of Twitter messages by President Trump. Digit Gov Res Pract. 2020 Nov 9;2(1):1-7.
18. Sharma A, Ghose U. Sentimental analysis of Twitter data with respect to general elections in India. Procedia Comput Sci. 2020 Jan 1;173:325-34.
19. Sandoval-Almazan R, Valle-Cruz D. Sentiment analysis of Facebook users reacting to political campaign posts. Digit Gov Res Pract. 2020 Apr 8;1(2):1-3.
20. Goel A, Mittal A. Stock prediction using Twitter sentiment analysis. Standford University; 2012 Sep. 2352 p.
21. Shen J, Najand M, Dong F, He W. News and social media emotions in the commodity market. Rev Behav Financ. 2017 Jul 10;9(2):148-68.
22. Pudaruth S, Moheeputh S, Permessur N, Chamroo A. Sentiment analysis from Facebook comments using automatic coding in NVivo 11. Adv Distrib Comput Artif Intell J. 2018;7(1):41-8.
23. Kathiravan C, Rajasekar A, Velmurgan S, Mahalakshmi P, Chandramouli E. Sentiment analysis and text mining of online customer reviews for digital wallet apps of Fintech industry. Int J Aquat Sci. 2021 Jun 1;12(3):2139-50.
24. Mendoza M, Poblete B, Castillo C. Twitter under crisis: can we trust what we RT? SOMA 2010, Proceedings of the 1st Workshop on Social Media Analytics; 2010 Jul 25. p. 71-9.
25. Singh M, Jakhar AK, Pandey S. Sentiment analysis on the impact of coronavirus in social life using the BERT model. Soc Netw Anal Min. 2021 Dec;11(1):33.
26. Arun K, Srinagesh A, Ramesh M. Twitter sentiment analysis on demonetization an initiative Government of India. Int J Recent Trends Eng Res. 2017 Jun 1;4(6):252-8.
27. Gul S, Shueb S. Twitter as a mourning dove for the blooming buds: an analysis of Twitter's reaction to Peshawar school attack. In: Singh J, Kerr P, Hamburger E, editors. Media and information literacy: reinforcing human rights, countering radicalization and extremism. UNESCO; 2016. p. 237-50.
28. Yu Y, Wang X. World Cup 2014 in the Twitter world: a big data analysis of sentiments in U.S. sports fans’ tweets. Comput Hum Behav. 2015 Jul 1;48:392-400.
