Identifying Degree-of-Concern on COVID-19 topics with text classification of Twitters
DOI:
https://doi.org/10.26594/register.v7i1.2234Keywords:
COVID-19, degree-of-concern, Deep Learning, Twitter text classification, word embeddingAbstract
The COVID-19 pandemic has various impacts on changing people’s behavior socially and individually. This study identifies the Degree-of-Concern topic of COVID-19 through citizen conversations on Twitter. It aims to help related parties make policies for developing appropriate emergency response strategies in dealing with changes in people’s behavior due to the pandemic. The object of research is 12,000 data from verified Twitter accounts in Surabaya. The varied nature of Twitter needs to be classified to address specific COVID-19 topics. The first stage of classification is to separate Twitter data into COVID-19 and non-COVID-19. The second stage is to classify the COVID-19 data into seven classes: warnings and suggestions, notification of information, donations, emotional support, seeking help, criticism, and hoaxes. Classification is carried out using a combination of word embedding (Word2Vec and fastText) and deep learning methods (CNN, RNN, and LSTM). The trial was carried out with three scenarios with different numbers of train data for each scenario. The classification results show the highest accuracy is 97.3% and 99.4% for the first and second stage classification obtained from the combination of fastText and LSTM. The results show that the classification of the COVID-19 topic can be used to identify Degree-of-Concern properly. The results of the Degree-of-Concern identification based on the classification can be used as a basis for related parties in making policies to formulate appropriate emergency response strategies in dealing with changes in public behavior due to a pandemic.References
[1] X. Zhu, S. Wu, D. Miao and Y. Li, "Changes in Emotion of The Chinese Public in Regard to the Sars Period," Social Behavior and Personality: an international journal, vol. 36, no. 4, pp. 447-454, 2008.
[2] X. Ji, S. A. Chun, Z. Wei and J. Geller, "Twitter sentiment classification for measuring public health concerns," Soc. Netw. Anal. Min., vol. 5, no. 13, 2015.
[3] K. Lee, D. Palsetia, R. Narayanan, M. M. A. Patwary, A. Agrawal and A. Choudhary, "Twitter Trending Topic Classification," in 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada, 2011.
[4] E. M. Glowacki, J. B. Glowacki and G. B. Wilcox, "A text-mining analysis of the public's reactions to the opioid crisis," Substance Abuse, vol. 39, no. 2, pp. 129-133, 2018.
[5] T. Rathod and M. Barot, "Trend Analysis on Twitter for Predicting Public Opinion on Ongoing Events," International Journal of Computer Applications, vol. 180, no. 26, pp. 13-17, 2018.
[6] L. Yan and A. J. Pedraza‐Martinez, "Social Media for Disaster Management: Operational Value of the Social Conversation," Production and Operations Management , vol. 28, no. 10, pp. 2514-2532, 2019.
[7] S. Vieweg, A. L. Hughes, K. Starbird and L. Palen, "Microblogging During Two Natural Hazards Events: What Twitter May Contribute to Situational Awareness," in CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, 2010.
[8] S. Kemp, "Digital 2019: Indonesia," DataReportal, 2019.
[9] C. Chew and G. Eysenbach, "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLoS ONE, vol. 5, no. 11, p. e14118, 2010.
[10] X. Ji, S. A. Chun and J. Geller, "Monitoring Public Health Concerns Using Twitter Sentiment Classifications," in 2013 IEEE International Conference on Healthcare Informatics, Philadelphia, PA, USA, 2013.
[11] L. Li, Q. Zhang, X. Wang, J. Zhang, T. Wang, T.-L. Gao, W. Duan, K. K.-f. Tsoi and F.-Y. Wan, "Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo," IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 556-562, 2020.
[12] S. Boukil, M. Biniz, F. E. Adnani, L. Cherrat and A. E. E. Moutaouakkil, "Arabic Text Classification Using Deep Learning Technics," International Journal of Grid and Distributed Computing, vol. 11, no. 9, pp. 103-114, 2018.
[13] R. A. Calix, R. Gupta, M. Gupta and K. Jiang, "Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning," in 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, 2017.
[14] Y. Kim, "Convolutional Neural Networks for Sentence Classification," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014.
[15] M. Hughes, I. Li, S. Kotoulas and T. Suzumura, "Medical Text Classification Using Convolutional Neural Networks," Stud Health Technol Inform, vol. 235, pp. 246-250, 2017.
[16] A. Severyn and A. Moschitti, "UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification," in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, 2015.
[17] K. Cho, B. v. Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk and Y. Bengio, "Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation," in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014.
[18] A. Tholusuri, M. Anumala, B. Malapolu and G. J. Lakshmi, "Sentiment Analysis using LSTM," International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 6S3, pp. 1338-1340, 2019.
[19] A. Rao and N. Spasojevic, "Actionable and Political Text Classification using Word Embeddings and LSTM," arXiv, 2016.
[20] B. Wang, A. Wang, F. Chen, Y. Wang and C.-C. J. Kuo, "Evaluating word embedding models: Methods and experimental results," APSIPA Transactions on Signal and Information Processing, vol. 8, no. E19, 2019.
[21] B. Kuyumcu, C. Aksakalli and S. Delil, "An automated new approach in fast text classification (fastText): A case study for Turkish text classification without pre-processing," in NLPIR 2019: Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval, Tokushima, Japan, 2019.
[22] F. K. Khattak, S. Jeblee, C. Pou-Prom, M. Abdalla, C. Meaney and F. Rudzicz, "A survey of word embeddings for clinical text," Journal of Biomedical Informatics: X, vol. 4, 2019.
[23] A. Mandelbaum and A. Shalev, "Word Embeddings and Their Use In Sentence Classification Tasks," arXiv, 2016.
[24] T. Mikolov, K. Chen, G. Corrado and J. Dean, "Efficient Estimation of Word Representations in Vector Space," arXiv, 2013.
[25] I. W. S. E. Putra, "Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) Pada Caltech 101," Institut Teknologi Sepuluh Nopember, Surabaya, 2016.
[26] P. Liu, X. Qiu and X. Huang, "Recurrent neural network for text classification with multi-task learning," in IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016.
[27] D. A. Nasution, H. H. Khotimah and N. Chamidah, "Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma k-NN," CESS (Journal of Computer Engineering, System and Science), vol. 4, no. 1, pp. 78-82, 2019.
[28] S. Dabiri and K. Heaslip, "Developing a Twitter-based traffic event detection model using deep learning architectures," Expert Systems with Applications, vol. 118, pp. 425-439, 2019.
[29] C. Zhou, C. Sun, Z. Liu and F. C. Lau, "A C-LSTM Neural Network for Text Classification," arXiv, 2015.
[30] Z. Zhang, Q. He, J. Gao and M. Ni, "A deep learning approach for detecting traffic accidents from social media data," Transportation Research Part C: Emerging Technologies, vol. 86, pp. 580-596, 2018.
[31] G. Liu, X. Xu, B. Deng, S. Chen and L. Li, "A hybrid method for bilingual text sentiment classification based on deep learning," in 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, China, 2016.
[32] C. Du and L. Huang, "Text Classification Research with Attention-based Recurrent Neural Networks," International Journal of Computers Communications & Control, vol. 13, no. 1, pp. 50-61, 2018.
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