Have a personal or library account? Click to login
Sentiment Analysis of Tweets on Coronavirus Disease 2019 (COVID-19) Pandemic from Metro Manila, Philippines Cover

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

Open Access
|Dec 2020

References

  1. 1. Wu, Z., J. M. McGoogan. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases from the Chinese Center for Disease Control and Prevention. – JAMA, 2020.10.1001/jama.2020.2648
  2. 2. Yang, Y., F. Peng, R. Wang, K. Guan, T. Jiang, G. Xu, J. Sun, C. Chang. The Deadly Coronaviruses: The 2003 Sars Pandemic and the 2020 Novel Coronavirus Epidemic in China. – Journal of Autoimmunity, 2020, p. 102434.10.1016/j.jaut.2020.102434
  3. 3. Phan, T. Novel Coronavirus: From Discovery to Clinical Diagnostics. – Infection, Genetics and Evolution, Vol. 79, 2020, p. 104211.10.1016/j.meegid.2020.104211
  4. 4. Sohrabi, C., Z. Alsafi, N. O’Neill, M. Khan, A. Kerwan, A. Al-Jabir, C. Iosifidis, R. Agha. World Health Organization Declares Global Emergency: A Review of the 2019 Novel Coronavirus (Covid-19). – International Journal of Surgery, Vol. 76, 2020.10.1016/j.ijsu.2020.02.034
  5. 5. Lima, C. K. T., P. M. d. M. Carvalho, I. d. A. S. Lima, J. V. A. d. O. Nunes, J. S. Saraiva, R. I. de Souza, C. G. L. da Silva, M. L. R. Neto. The Emotional Impact of Coronavirus 2019-Ncov (New Coronavirus Disease). – Psychiatry Research, 2020, p. 112915.10.1016/j.psychres.2020.112915
  6. 6. Brooks, S. K., R. K. Webster, L. E. Smith, L. Woodland, S. Wessely, N. Greenberg, G. J. Rubin. The Psychological Impact of Quarantine and How to Reduce It: Rapid Review of the Evidence. – The Lancet, Vol. 395, 2020, No 10227, pp. 912-920.10.1016/S0140-6736(20)30460-8
  7. 7. Villapando, A. Prrd Orders “Stringent Social Distancing Measures” in Ncr., 2020. https://pia.gov.ph/news/articles/1036195
  8. 8. Zhao, Y., J. Zhang. Consumer Health Information Seeking in Social Media: A Literature Review. – Health Information & Libraries Journal, Vol. 34, 2017, No 4, pp. 268-283.10.1111/hir.1219229045011
  9. 9. Brandtzæg, P. B., J. Heim. Why People Use Social Networking Sites. Berlin, Heidelberg, Springer, 2009, pp. 143-152.10.1007/978-3-642-02774-1_16
  10. 10. Liu, B. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.10.2200/S00416ED1V01Y201204HLT016
  11. 11. Wang, Y., M. S. M. Pozi, Y. Kawai, A. Jatowt, T. Akiyama. Exploring Cross-Cultural Crowd Sentiments on Twitter. – In: Proc. of 28th ACM Conference on Hypertext and Social Media, Prague, Czech Republic, 2017.10.1145/3078714.3078749
  12. 12. Kordonis, J., S. Symeonidis, A. Arampatzis. Stock Price Forecasting via Sentiment Analysis on Twitter. – In: Proc. of 20th Pan-Hellenic Conference on Informatics, Patras, Greece, 2016. https://doi.org/10.1145/3003733.300378710.1145/3003733.3003787
  13. 13. Kanavos, A., N. Nodarakis, S. Sioutas, A. Tsakalidis, D. Tsolis, G. Tzimas. Large Scale Implementations for Twitter Sentiment Classification. – Algorithms, 2017.10.3390/a10010033
  14. 14. Krauss, J., S. Nann, D. Simon, K. Fischbach, P. Gloor. Predicting Movie Success and Academy Awards through Sentiment and Social Network Analysis. – In: Proc. of European Conference in Information Systems, 2008.
  15. 15. Wang, H., D. Can, A. Kazemzadeh, F. Bar, S. Narayanan. A System for Real-Time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle. – In: Proc. of ACL 2012 System Demonstrations, Jeju Island, Korea, 2012.
  16. 16. Karami, A., V. Shah, R. Vaezi, A. Bansal. Twitter Speaks: A Case of National Disaster Situational Awareness. – Journal of Information Science, 2019.10.1177/0165551519828620
  17. 17. Yin, J., A. Lampert, M. Cameron, B. Robinson, R. Power. Using Social Media to Enhance Emergency Situation Awareness. – IEEE Intelligent Systems, Vol. 27, 2012.10.1109/MIS.2012.6
  18. 18. Qu, Y., C. Huang, P. Zhang, J. Zhang. Microblogging after a Major Disaster in China: A Case Study of the 2010 Yushu Earthquake. – In: Proc. of ACM 2011 Conference on Computer Supported Cooperative Work, Hangzhou, China, 2011.10.1145/1958824.1958830
  19. 19. Sakaki, T., M. Okazaki, Y. Matsuo. Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors. – In: Proc. of 19th International Conference on World Wide Web, Raleigh, North Carolina, USA, 2010.10.1145/1772690.1772777
  20. 20. Vieweg, S. E. Situational Awareness in Mass Emergency: A Behavioral and Linguistic Analysis of Microblogged Communications. Doctor of Philosophy in Technology, Media and Society, Graduate School, University of Colorado, Boulder, Colorado, 2012.
  21. 21. Imran, M., C. Castillo, F. Diaz, S. Vieweg. Processing Social Media Messages in Mass Emergency: A Survey. – ACM Comput. Surv., Vol. 47, 2015, No 4, Article 67.10.1145/2771588
  22. 22. Qu, Y., P. F. Wu, X. Wang. Online Community Response to Major Disaster: A Study of Tianya Forum in the 2008 Sichuan Earthquake. – In: Proc. of 42nd Hawaii International Conference on System Sciences, 2009, pp. 1-11.
  23. 23. Starbird, K., L. Palen, A. L. Hughes, S. Vieweg. Chatter on the Red: What Hazards Threat Reveals About the Social Life of Microblogged Information. – In: Proc. of 2010 ACM Conference on Computer Supported Cooperative Work, Savannah, Georgia, USA, 2010. https://doi.org/10.1145/1718918.171896510.1145/1718918.1718965
  24. 24. Vieweg, S., A. L. Hughes, K. Starbird, L. Palen. Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness. – In: Proc. of SIGCHI Conference on Human Factors in Computing Systems, Atlanta, Georgia, USA, 2010. https://doi.org/10.1145/1753326.1753486.10.1145/1753326.1753486
  25. 25. Imran, M., S. Elbassuoni, C. Castillo, F. Diaz, P. Meier. Practical Extraction of Disaster-Relevant Information from Social Media. – In: Proc. of 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 2013.10.1145/2487788.2488109
  26. 26. Truong, B., C. Caragea, A. Squicciarini, A. H. Tapia. Identifying Valuable Information from Twitter during Natural Disasters. – Proceedings of the American Society for Information Science and Technology, Vol. 51, 2014, No 1, pp. 1-4.10.1002/meet.2014.14505101162
  27. 27. Ren, D., X. Zhang, Z. Wang, J. Li, X. Yuan. Weiboevents: A Crowd Sourcing Weibo Visual Analytic System. – In: Proc. of 2014 IEEE Pacific Visualization Symposium, 2014, pp. 330-334.
  28. 28. Morstatter, F., S. Kumar, H. Liu, R. Maciejewski. Understanding Twitter Data with Tweetxplorer. – In: Proc. of 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, 2013.10.1145/2487575.2487703
  29. 29. MacEachren, A. M., A. Jaiswal, A. C. Robinson, S. Pezanowski, A. Savelyev, P. Mitra, J. Zhang, J. Blanford. Senseplace2: GeoTwitter Analytics Support for Situational Awareness. – In: Proc. of 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), 2011, pp. 181-190.10.1109/VAST.2011.6102456
  30. 30. Calderon, N. A., R. Arias-Hernandez, B. Fisher. Studying Animation for Real-Time Visual Analytics: A Design Study of Social Media Analytics in Emergency Management. – In: Proc. of 47th Hawaii International Conference on System Sciences, 2014, pp. 1364-1373.10.1109/HICSS.2014.176
  31. 31. Abel, F., C. Hauff, G.-J. Houben, R. Stronkman, K. Tao. Semantics + Filtering + Search = Twitcident. Exploring Information in Social Web Streams. – In: Proc. of 23rd ACM Conference on Hypertext and Social Media, Milwaukee, Wisconsin, USA, 2012.10.1145/2309996.2310043
  32. 32. Soriano, C. R., M. D. G. Roldan, C. Cheng, N. Oco. Social Media and Civic Engagement During Calamities: The Case of Twitter Use During Typhoon Yolanda. – Philippine Political Science Journal, Vol. 37, 2016, No 1, pp. 6-25.10.1080/01154451.2016.1146486
  33. 33. Neppalli, V. K., C. Caragea, A. Squicciarini, A. Tapia, S. Stehle. Sentiment Analysis During Hurricane Sandy in Emergency Response. – International Journal of Disaster Risk Reduction, Vol. 21, 2017, pp. 213-222.10.1016/j.ijdrr.2016.12.011
  34. 34. Verma, S., S. Vieweg, W. J. Corvey, L. Palen, J. H. Martin, M. Palmer, A. Schram, K. M. Anderson. Natural Language Processing to the Rescue? Extracting “Situational Awareness” Tweets during Mass Emergency. – In: Proc. of International Conference on Weblogs and Social Media, 2011.
  35. 35. Kaur. A. Analyzing Twitter Feeds to Facilitate Crises Informatics and Disaster Response During Mass Emergencies. M. Sc. in Computing (Data Analytics), Dublin Institute of Technology, TU Dublin, 2019.
  36. 36. Pang, B., L. Lee. Opinion Mining and Sentiment Analysis. – Foundations and Trends in Information Retrieval, Vol. 2, 2008, No 1-2, pp. 1-135.10.1561/1500000011
  37. 37. To, H., S. Agrawal, S. H. Kim, C. Shahabi. On Identifying Disaster-Related Tweets: Matching-Based or Learning-Based?. – In: Proc. of 2017 IEEE Third International Conference on Multimedia Big Data (BigMM), 2017, pp. 330-337.10.1109/BigMM.2017.82
  38. 38. Hasan, A., S. Moin, A. Karim, S. Shamshirband. Machine Learning-Based Sentiment Analysis for Twitter Accounts. – Mathematical and Computational Applications, 2018.10.3390/mca23010011
  39. 39. Pang, B., L. Lee, S. Vaithyanathan. Thumbs Up? Sentiment Classification Using Machine Learning Techniques. – In: Proc. of ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol. 10, 2002.10.3115/1118693.1118704
  40. 40. Pang, B., L. Lee. A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. – In: Proc. of 42nd Annual Meeting on Association for Computational Linguistics, Barcelona, Spain, 2004.10.3115/1218955.1218990
  41. 41. Nielsen, F. A. A New Anew: Evaluation of a Word List for Sentiment Analysis in Microblogs. – In: Proc. of ESWC Workshop on “Making Sense of Microposts”: Big Things Come in Small Packages, Heraklion, Crete, 2011.
  42. 42. Baccianella, S., A. Esuli, F. Sebastiani. Sentiwordnet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. – In: Proc. of 7th International Conference on Language Resources and Evaluation, Valletta, Malta, 2010.
  43. 43. Rout, J. K., K.-K. R. Choo, A. K. Dash, S. Bakshi, S. K. Jena, K. L. Williams. A Model for Sentiment and Emotion Analysis of Unstructured Social Media Text. – Electronic Commerce Research, Vol. 18, 2018, No 1, pp. 181-199.10.1007/s10660-017-9257-8
  44. 44. Taboada, M., J. Brooke, M. Tofiloski, K. Voll, M. Stede. Lexicon-Based Methods for Sentiment Analysis. – Computational Linguistics, Vol. 37, 2011, No 2, pp. 267-307.10.1162/COLI_a_00049
  45. 45. Zhou, Z., X. Zhang, M. Sanderson. Sentiment Analysis on Twitter through Topic-Based Lexicon Expansion. – Springer International Publishing, Cham, 2014, pp. 98-109.10.1007/978-3-319-08608-8_9
  46. 46. Jurek, A., M. D. Mulvenna, Y. Bi. Improved Lexicon-Based Sentiment Analysis for Social Media Analytics. – Security Informatics, Vol. 4, 2015, No 9.10.1186/s13388-015-0024-x
  47. 47. Ray, D. Lexicon Based Sentiment Analysis of Twitter Data. – International Journal for Research in Applied Science & Engineering Technology, 2017.10.22214/ijraset.2017.10130
  48. 48. Han, H., Y. Zhang, J. Zhang, J. Yang, X. Zou. Improving the Performance of Lexicon-Based Review Sentiment Analysis Method by Reducing Additional Introduced Sentiment Bias. – PLOS ONE, Vol. 13, 2018, No 8, p. e0202523.10.1371/journal.pone.0202523610845830142154
  49. 49. Naldi, M. A Review of Sentiment Computation Methods with R Packages. – arXiv, 2019.
  50. 50. Davies, S. E. National Security and Pandemics. – United Nation Chronicle, Vol. 50, 2013, No 2.10.18356/0dfec716-en
  51. 51. Price-Smith, A. T. Contagion and Chaos: Disease, Ecology, and National Security in the Era of Globalization. Cambridge, MA, MIT Press, 2009.10.7551/mitpress/7390.001.0001
  52. 52. Qiu, W., S. Rutherford, A. Mao, C. Chu. The Pandemic and Its Impacts. – Health, Culture, and Society, 2016.10.5195/HCS.2017.221
  53. 53. Garcia, M. B. e-Learning Technology Adoption in the Philippines: An Investigation of Factors Affecting Filipino College Students’ Acceptance of Learning Management Systems. –International Journal of e-Learning and Educational Technologies in the Digital Media (IJEETDM), Vol. 3, 2017, No 3, pp. 118-130.10.17781/P002374
  54. 54. Lau, J. T. F., X. Yang, E. Pang, H. Y. Tsui, E. Wong, Y. K. Wing. Sars-Related Perceptions in Hong Kong. – Emerging Infectious Diseases, Vol. 11, 2005, No 3, pp. 417-424.10.3201/eid1103.040675
  55. 55. Fielding, R., W. T. Lam Wendy. Reducing Avian Influenza Risk: A Qualitative Exploration of Issues in Hong Kong. – Health Education, Vol. 107, 2007, No 5, pp. 437-447.10.1108/09654280710778574
  56. 56. Rubin, G. J., R. Amlôt, L. Page, S. Wessely. Public Perceptions, Anxiety, and Behaviour Change in Relation to the Swine Flu Outbreak: Cross Sectional Telephone Survey. – BMJ, Vol. 339, 2009, p. b2651.10.1136/bmj.b2651271468719574308
  57. 57. Bish, A., S. Michie. Demographic and Attitudinal Determinants of Protective Behaviours During a Pandemic: A Review. – British Journal of Health Psychology, Vol. 15, 2010, No 4.10.1348/135910710X485826718545220109274
  58. 58. Pickles, H. Using Lessons from the Past to Plan for Pandemic Flu. – BMJ (Clinical Research Ed.), Vol. 332, 2006, No 7544, pp. 783-786.10.1136/bmj.332.7544.783142070216575084
  59. 59. Centers for Disease Control and Prevention. “Pandemic Influenza – Past, Present, Future: Communicating Today Based on the Lessons from the 1918-1919 Influenza Pandemic”. U.S. Department of Health and Human Services Centers for Disease Control and Prevention Washington, DC 2006.
  60. 60. Yoon, M.-K., S.-Y. Kim, H.-S. Ko, M.-S. Lee. System Effectiveness of Detection, Brief Intervention and Refer to Treatment for the People with Post-Traumatic Emotional Distress by Mers: A Case Report of Community-Based Proactive Intervention in South Korea. – International Journal of Mental Health Systems, Vol. 10, 2016, No 1, p. 51.10.1186/s13033-016-0083-5497650527504141
  61. 61. Lee, S., L. Y. Y. Chan, A. M. Y. Chau, K. P. S. Kwok, A. Kleinman. The Experience of Sars-Related Stigma at Amoy Gardens. – Social Science & Medicine, Vol. 61, 2005, No 9, pp. 2038-2046.10.1016/j.socscimed.2005.04.010711697515913861
  62. 62. Hawryluck, L., W. L. Gold, S. Robinson, S. Pogorski, S. Galea, R. Styra. Sars Control and Psychological Effects of Quarantine, Toronto, Canada. – Emerging Infectious Diseases, Vol. 10, 2004, No 7, pp. 1206-1212.10.3201/eid1007.030703332334515324539
  63. 63. GMA News. Manila Mayor Isko Moreno Implements Covid-19 Action Plan. 2020.https://www.gmanetwork.com/news/news/metro/729490/manila-mayor-isko-moreno-implements-covid-19-action-plan/story/
DOI: https://doi.org/10.2478/cait-2020-0052 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 141 - 155
Submitted on: Mar 17, 2020
|
Accepted on: Oct 7, 2020
|
Published on: Dec 10, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Manuel B. Garcia, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.