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Sentiment Analysis on Global Events under Pandemic of COVID-19

  • Junjun, Zhang (Dept. of Computer Information Engineering, Cheongju Univ.) ;
  • Noh, Giseop (Division of Software Convergence, Cheongju Univ.)
  • Received : 2022.06.02
  • Accepted : 2022.09.01
  • Published : 2022.09.30

Abstract

During last few years, pandemic of COVID-19 has been a global issue. Under the COVID-19, global events have been restricted or canceled to secure public hygiene and safety. Since one of the largest global events is Olympic Games, we selected recent Olympic Games as our case of analysis. Tokyo Olympic Games (TOG) was held in 2021, but it encountered a millennium disaster, the pandemic of COVID-19. In such a special period, it is of great significance to explore the emotional tendency of global views before and TOG via artificial intelligence. This paper vastly collects the TOG comment data of mainstream websites in South Korea, China, and the United States by implementing crawler program for sentiment analysis (SA). And we use a variety of sentiment analysis models to compare the accuracy of the experimental results, to obtain more reliable SA results. In addition, in the prediction results, to reduce the distortion of opinion by a minority, we introduce an algorithm called "Removing Biased Minority Opinions (RBMO)" and provide how to apply this method to the interpretation domain. Through our method, more authoritative SA results were obtained, which in turn provided a basis for predicting the sentiment tendency of countries around the world in TOG during the COVID-19 epidemic.

Keywords

References

  1. A. Vaswani et al., "Attention is all you need," in Advances in neural information processing systems, pp. 5998-6008, 2017.
  2. S.-J. Yea, S. Kim, T. John-Michael, and J.-G. Lee, "SentiWorld: Understanding Emotions between Countries Based on Tweets," in Tenth International AAAI Conference on Web and Social Media, 2016.
  3. P. S. Dodds, K. D. Harris, I. M. Kloumann, C. A. Bliss, and C. M. Danforth, "Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter," PloS one, vol. 6, no. 12, p. e26752, 2011.
  4. M. Mansoor, K. Gurumurthy, and V. Prasad, "Global Sentiment Analysis Of COVID-19 Tweets Over Time," arXiv preprint arXiv:2010.14234, 2020.
  5. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  6. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding," arXiv preprint arXiv:1810.04805, 2018.
  7. V. S. Subrahmanian and D. Reforgiato, "AVA: Adjective-Verb-Adverb Combinations for Sentiment Analysis," IEEE Intelligent Systems, vol. 23, no. 4, pp. 43-50, 2008. https://doi.org/10.1109/MIS.2008.57
  8. Z. Gong-rang, B. Chao, W. Xiao-yu, G. Dong-xiao, Y. Xue-jie, and L. Kang, "Sentiment Analysis and Text Data Mining Based on Reviewing Data," Information Science, vol. 39, no. 5, pp. 53-61, May 2021.
  9. H. Keshavarz and M. S. Abadeh, "ALGA: Adaptive lexicon learning using genetic algorithm for sentiment analysis of microblogs," Knowledge-Based Systems, vol. 122, pp. 1-16, April 2017. https://doi.org/10.1016/j.knosys.2017.01.028
  10. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up? Sentiment classification using machine learning techniques," arXiv preprint cs/0205070, 2002.
  11. J. Zhang, C. Zhao, F. Xu, and P. Zhang, "SVM-Based Sentiment Analysis Algorithm of Chinese Microblog Under Complex Sentence Pattern," International Conference in Communications, Signal Processing, and Systems Springer, pp. 801-809, Singapore, 2018.
  12. Y. Kim, "Convolutional Neural Networks for Sentence Classification," Doha. Association for Computational Linguistics, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746-1751, Oct 2014.
  13. P. Liu, X. Qiu, and X. Huang, "Recurrent neural network for text classification with multi-task learning," arXiv preprint arXiv:1605.05101, 2016.
  14. K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014.
  15. G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, "Sentiment Analysis of Comment Texts Based on BiLSTM," IEEE Access, vol. 7, pp. 51522-51532, 2019. https://doi.org/10.1109/access.2019.2909919
  16. Z. Cui, J. Zhang, G.Noh, HJ.Park, "MFDGCN: Multi-Stage Spatio-Temporal Fusion Diffusion Graph Convolutional Network for Traffic Prediction," Applied Sciences, Vol. 12, No. 2, PP. 2688,March 2022.
  17. L. Guo, D. Zhang, L. Wang, H. Wang, and B. Cui, "CRAN: a hybrid CNN-RNN attention-based model for text classification," in International Conference on Conceptual Modeling, Springer, pp. 571-585, 2018.
  18. X. She and D. Zhang, "Text classification based on hybrid CNN-LSTM hybrid model," 2018 11th International Symposium on Computational Intelligence and Design (ISCID), IEEE, vol. 2, pp. 185-189, 2018.
  19. J. Zhang, Y. Li, J. Tian, and T. Li, "LSTM-CNN hybrid model for text classification," 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, pp. 1675-1680, 2018.
  20. D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," arXiv preprint arXiv:1409.0473, 2014.
  21. Z. Niu, G. Zhong, and H. Yu, "A review on the attention mechanism of deep learning," Neurocomputing, vol. 452, pp. 48-62, September 2021. https://doi.org/10.1016/j.neucom.2021.03.091
  22. Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, "Xlnet: Generalized autoregressive pretraining for language understanding," Advances in neural information processing systems, vol. 32, 2019.
  23. I. Santos, N. Nedjah, and L. d. M. Mourelle, "Sentiment analysis using convolutional neural network with fastText embeddings," Arequipa, Peru, 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1-5, Nov. 8-10, 2017.
  24. T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
  25. P. Masurel, Of bayesian average and star ratings. https://fulmicoton.com/posts/bayesian_rating/ (accessed Dec. 16, 2021).
  26. C. Study. "What algorithm does IMDB use for ranking the movies on its site?" Quora. https://www.quora.com/What-algorithm-does-IMDB-use-for-ranking-the-movies-on-its-site (accessed Dec. 14, 2021).