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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 2

Sentiment Analysis of Monkeypox Tweets Using a Hybrid CNN-BiGRU Based Deep Learning Model

Ümit Can(MUNZUR ÜNİVERSİTESİ)0000-0002-8832-6317
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Recently, epidemics have disrupted people's lives.
📊
Key Findings & Data:Following the COVID-19 pandemic, cases of monkeypox, a rare disease, began to appear worldwide.
💡
Conclusion & Impact:The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Deep learning · Machine learning · Monkeypox

Abstract

Recently, epidemics have disrupted people's lives. Following the COVID-19 pandemic, cases of monkeypox, a rare disease, began to appear worldwide. These incidents raised societal tension and anxiety. Understanding public attitudes, sentiments, and perceptions regarding the epidemic is essential for developing and implementing effective policies and countermeasures. Online social media platforms are essential venues for expressing feelings and views. In particular, Twitter provides valuable information for measuring societal sentiment. Machine learning and deep learning methods have recently been widely used in social network analysis. Deep learning models, which deliver strong performance, particularly on complex and nonlinear problems, are used in natural language processing and text mining. In this study, a Convolutional Neural Network Bidirectional Gated Recurrent Unit (CNN-BiGRU) hybrid deep learning model was developed by combining a CNN and a BiGRU and applied for the monkeypox sentiment analysis. The dataset was labeled as positive, negative, or neutral with Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, FastText and Term Frequency-Inverse Document Frequency (TF-IDF) word-embedding methods were used for feature extraction, and the performance of machine-learning and deep-learning methods was evaluated separately. As a result of the experimental studies, the FastText-CNN-BiGRU model obtained the highest results with 0.9382 accuracy, 0.9347 macro precision, 0.9367 macro recall, 0.9355 macro F-score, and 0.9875 macro ROC-AUC. The FastText-CNN model was the second most successful, achieving 0.9303 accuracy, 0.9268 macro precision, 0.9280 macro recall, 0.9271 macro F-score, and 0.9862 macro ROC-AUC. The FastText-BiGRU model was the third most successful, achieving 0.9262 accuracy, 0.9224 macro precision, 0.9255 macro recall, 9236 macro F-score, and 0.9838 macro ROC-AUC.

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Cite This Article

Can, Ü. (2026). Sentiment Analysis of Monkeypox Tweets Using a Hybrid CNN-BiGRU Based Deep Learning Model. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 2. https://doi.org/10.17798/bitlisfen.1842980

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References (64)

  1. E. Alakunle, U. Moens, G. Nchinda, and M. I. Okeke, “Monkeypox virus in Nigeria: infection biology, epidemiology, and evolution,” Viruses, vol. 12, no. 11, p. 1257, 2020.
  2. P. V. Magnus, E. K. Andersen, K. B. Petersen, and A. Birch‐Andersen, “A pox‐like disease in cynomolgus monkeys,” Acta Pathologica Microbiologica Scandinavica, vol. 46, no. 2, pp. 156–176, 1959.
  3. E. M. Bunge et al., “The changing epidemiology of human monkeypox—A potential threat? A systematic review,” PLoS Neglected Tropical Diseases, vol. 16, no. 2, p. e0010141, 2022.
  4. J. Quarleri, M. V. Delpino, and V. Galvan, “Monkeypox: considerations for the understanding and containment of the current outbreak in non-endemic countries,” Geroscience, vol. 44, no. 4, pp. 2095–2103, 2022.
  5. “Mpox Outbreak Global Map,” Centers for Disease Control and Prevention (CDC), 2022. [Online]. Available: https://www.cdc.gov/poxvirus/monkeypox/response/2022/world-map.html. [Accessed: Feb. 1, 2023].
  6. J. A. Roberts et al., “The study of infectious intestinal disease in England: socio-economic impact,” Epidemiology and Infection, vol. 130, no. 1, p. 1, 2003.
  7. X. Li and H. Lyu, “Epidemic risk perception, perceived stress, and mental health during COVID-19 pandemic: A moderated mediating model,” Frontiers in Psychology, vol. 11, p. 563741, 2021.
  8. A. Montoyo, P. Martínez-Barco, and A. Balahur, “Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments,” Decision Support Systems, vol. 53, no. 4, pp. 675–679, 2012.
  9. W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014.
  10. A. Giachanou and F. Crestani, “Like it or not: A survey of twitter sentiment analysis methods,” ACM Computing Surveys, vol. 49, no. 2, pp. 1–41, 2016.
  11. U. Naseem, I. Razzak, M. Khushi, P. W. Eklund, and J. Kim, “COVIDSenti: A large-scale benchmark Twitter data set for COVID-19 sentiment analysis,” IEEE Trans. Comput. Social Syst., vol. 8, no. 4, pp. 1003–1015, 2021.
  12. S. A. Morshed, S. S. Khan, R. B. Tanvir, and S. Nur, “Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysis,” Journal of Urban Management, vol. 10, no. 2, pp. 155–165, 2021.
  13. P. Sv and R. Ittamalla, “What concerns the general public the most about monkeypox virus?–A text analytics study based on Natural Language Processing (NLP),” Travel Medicine and Infectious Disease, 2022.
  14. H. Tekin, Ş. Kılıç, and Y. Doğan, “DiagNeXt: A Two-Stage Attention-Guided ConvNeXt Framework for Kidney Pathology Segmentation and Classification,” Journal of Imaging, 11(12), 433, 2025.
  15. Y. Çelik, “Application of Deep Learning for Voice Command Classification in Turkish Language,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 13(3), 701-708, 2024.
  16. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proc. Workshop at ICLR, Scottsdale, AZ, USA, 2013.
  17. J. Brown, A. J. Broderick, and N. Lee, “Word of mouth communication within online communities: Conceptualizing the online social network,” Journal of Interactive Marketing, vol. 21, no. 3, pp. 2–20, 2007.
  18. J. Heidemann, M. Klier, and F. Probst, “Online social networks: A survey of a global phenomenon,” Computer Networks, vol. 56, no. 18, pp. 3866–3878, 2012.
  19. Statista, “Global social networks ranked by number of users,” 2025. [Online]. Available: https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users. [Accessed: May 30, 2025].
  20. A. Keenan and A. Shiri, “Sociability and social interaction on social networking websites,” Library Review, vol. 58, no. 6, pp. 438–450, 2009.
  21. M. L. Kasavana, K. Nusair, and K. Teodosic, “Online social networking: redefining the human web,” Journal of Hospitality and Tourism Technology, vol. 1, no. 1, pp. 68–82, 2010.
  22. D. M. E. D. M. Hussein, “A survey on sentiment analysis challenges,” Journal of King Saud University – Engineering Sciences, vol. 30, no. 4, pp. 330–338, 2018.
  23. M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A survey on sentiment analysis methods, applications, and challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022.
  24. L. Zhang, S. Wang, and B. Liu, “Deep learning for sentiment analysis: A survey,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1253, 2018.
  25. S. Bhuta, A. Doshi, U. Doshi, and M. Narvekar, “A review of techniques for sentiment analysis of twitter data,” in Proc. 2014 Int. Conf. Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 583–591.
  26. T. Vijay, A. Chawla, B. Dhanka, and P. Karmakar, “Sentiment analysis on covid-19 twitter data,” in Proc. 2020 5th IEEE Int. Conf. Recent Advances and Innovations in Engineering (ICRAIE), 2020, pp. 1–7.
  27. S. Boon-Itt and Y. Skunkan, “Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study,” JMIR Public Health and Surveillance, vol. 6, no. 4, p. e21978, 2020.
  28. F. Rustam et al., “A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis,” PLOS ONE, vol. 16, no. 2, p. e0245909, 2021.
  29. Z. B. Nezhad and M. A. Deihimi, “Twitter sentiment analysis from Iran about COVID 19 vaccine,” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 16, no. 1, p. 102367, 2022.
  30. S. De Rosis, M. Lopreite, M. Puliga, and M. Vainieri, “The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis,” Health Policy, vol. 125, no. 8, pp. 987–994, 2021.
  31. M. E. Basiri, S. Nemati, M. Abdar, S. Asadi, and U. R. Acharya, “A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets,” Knowledge-Based Systems, vol. 228, p. 107242, 2021.
  32. C. Villavicencio et al., “Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes,” Information, vol. 12, no. 5, p. 204, 2021.
  33. H. Kaur, S. U. Ahsaan, B. Alankar, and V. Chang, “A proposed sentiment analysis deep learning algorithm for analyzing COVID-19 tweets,” Information Systems Frontiers, pp. 1–13, 2021.
  34. K. N. Alam et al., “Deep learning-based sentiment analysis of COVID-19 vaccination responses from Twitter data,” Computational and Mathematical Methods in Medicine, vol. 2021, 2021.
  35. D. Sunitha, R. K. Patra, N. V. Babu, A. Suresh, and S. C. Gupta, “Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries,” Pattern Recognition Letters, vol. 158, pp. 164–170, 2022.
  36. G. Chandrasekaran and J. Hemanth, “Deep learning and TextBlob based sentiment analysis for coronavirus (COVID-19) using twitter data,” International Journal on Artificial Intelligence Tools, vol. 31, no. 01, p. 2250011, 2022.
  37. C. Singh, T. Imam, S. Wibowo, and S. Grandhi, “A deep learning approach for sentiment analysis of COVID-19 reviews,” Applied Sciences, vol. 12, no. 8, p. 3709, 2022.
  38. S. Singh, H. Kaur, R. Kanozia, and G. Kaur, “Empirical analysis of supervised and unsupervised machine learning algorithms with aspect-based sentiment analysis,” Applied Computer Systems, vol. 28, no. 1, pp. 125–136, 2023.
  39. N. AlShammari and A. AlMansour, “Aspect-based sentiment analysis and location detection for Arabic language tweets,” Applied Computer Systems, vol. 27, no. 2, pp. 119–127, 2022.
  40. Q. X. Ng, C. E. Yau, Y. L. Lim, L. K. T. Wong, and T. M. Liew, “Public sentiment on the global outbreak of monkeypox: An unsupervised machine learning analysis of 352,182 twitter posts,” Public Health, vol. 213, pp. 1–4, 2022.
  41. V. S. Dsouza et al., “A sentiment and content analysis of tweets on monkeypox stigma among the LGBTQ+ community: A cue to risk communication plan,” Dialogues in Health, vol. 2, p. 100095, 2023.
  42. O. Iparraguirre-Villanueva et al., “The public health contribution of sentiment analysis of Monkeypox tweets to detect polarities using the CNN-LSTM model,” Vaccines, vol. 11, no. 2, p. 312, 2023.
  43. S. Bengesi, T. Oladunni, R. Olusegun, and H. Audu, “A machine learning-sentiment analysis on Monkeypox outbreak: An extensive dataset to show the polarity of public opinion from twitter tweets,” IEEE Access, vol. 11, pp. 11811–11826, 2023.
  44. K. K. Mohbey, G. Meena, S. Kumar, and K. Lokesh, “A CNN-LSTM-Based Hybrid Deep Learning Approach for Sentiment Analysis on Monkeypox Tweets,” New Generation Computing, pp. 1–19, 2023.
  45. “Monkeypox Tweets,” Kaggle dataset. [Online]. Available: https://www.kaggle.com/datasets/vencerlanz09/monkeypox-tweets. [Accessed: Dec. 5, 2025].
  46. S. Wang, W. Zhou, and C. Jiang, “A survey of word embeddings based on deep learning,” Computing, vol. 102, pp. 717–740, 2020.
  47. P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 135–146, 2017.
  48. H. C. Wu, R. W. P. Luk, K. F. Wong, and K. L. Kwok, “Interpreting TF-IDF term weights as making relevance decisions,” ACM Trans. Inf. Syst., vol. 26, no. 3, pp. 1–37, 2008.
  49. L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, 2001.
  50. G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, pp. 197–227, 2016.
  51. B. Xu, X. Guo, Y. Ye, and J. Cheng, “An improved random forest classifier for text categorization,” Journal of Computers, vol. 7, no. 12, pp. 2913–2920, 2012.
  52. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 2016, pp. 785–794.
  53. L. Brown and C. Mues, “An experimental comparison of classification algorithms for imbalanced credit scoring data sets,” Expert Systems with Applications, vol. 39, pp. 3446–3453, 2012.
  54. S. L, Ramaswamy, and J. Chinnappan, “Review on positional significance of LSTM and CNN in the multilayer deep neural architecture for efficient sentiment classification”. Journal of Intelligent & Fuzzy Systems, 45(4), 6077-6105, 2023.
  55. K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980.
  56. Y. Dogan, “A novel pruning-enhanced hybrid approach for efficient and accurate brain tumor diagnosis”. Biomedical Signal Processing and Control, 112, 108466, (2026).
  57. L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, pp. 1–74, 2021.
  58. Y. Dogan, “An innovative approach for parkinson’s disease diagnosis using CNN, NCA, and SVM”. Neural Computing and Applications, 36(32), 20089-20110, 2024.
  59. X. Zhang, F. Guo, T. Chen, L. Pan, G. Beliakov, and J. Wu, “A brief survey of machine learning and deep learning techniques for e-commerce research,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 18, no. 4, pp. 2188–2216, 2023.
  60. M. S. Islam, M. N. Kabir, N. A. Ghani, K. Z. Zamli, N. S. A. Zulkifli, M. M. Rahman, and M. A. Moni, “Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach,” Artificial Intelligence Review, vol. 57, no. 3, p. 62, 2024.
  61. H., Gharaibeh et al. “Arabic sentiment analysis of Monkeypox using deep neural network and optimized hyperparameters of machine learning algorithms,” Social Network Analysis and Mining, 14(1), 30, 2024.
  62. S. M. Lundberg and S. I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774, 2017.
  63. P. Rajkhowa, V. S. Dsouza, R. Kharel, K. Cauvery, B. R. Mallya, D. S. Raksha, et al., “Factors influencing monkeypox vaccination: a cue to policy implementation,” Journal of Epidemiology and Global Health, vol. 13, no. 2, pp. 226–238, 2023.
  64. E. T. Overton, S. J. Lawrence, J. T. Stapleton, H. Weidenthaler, D. Schmidt, B. Koenen, et al., “A randomized phase II trial to compare safety and immunogenicity of the MVA-BN smallpox vaccine at various doses in adults with a history of AIDS,” Vaccine, vol. 38, no. 11, pp. 2600–2607, 2020.