Sentiment and Public Emotion Classification of Viral Content Using Transformer-Based Model
DOI:
https://doi.org/10.35870/ijsecs.v6i1.6969Keywords:
Sentiment Analysis, Emotion Classification, Viral Content, Transformer, BERTAbstract
The proliferation of social media platforms has generated an unprecedented volume of viral content, each drawing varied public responses expressed through sentiment and emotion. Mapping those responses — not merely counting them — is what separates surface-level monitoring from a genuine understanding of public perception. This study classified sentiment (positive, negative, neutral) and emotion (anger, joy, sadness, and fear) toward viral content using a fine-tuned Transformer-based model. Data were collected from social media via web scraping, then subjected to standard text preprocessing: case folding, tokenization, stopword removal, and stemming. The cleaned dataset was subsequently annotated with sentiment and emotion labels. BERT (Bidirectional Encoder Representations from Transformers) served as the base architecture, fine-tuned for multi-label classification. Evaluation relied on an 80:20 train-test split, with performance measured through accuracy, precision, recall, and F1-score. Across all sentiment and emotion categories, the model returned consistently high scores and handled ambiguous, context-dependent text more reliably than conventional machine learning baselines. The Transformer-based approach proved well-suited for sentiment and emotion analysis on social media data, with clear potential for deployment in public opinion monitoring systems.
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Almalki, S. S. (2025). Sentiment analysis and emotion detection using transformer models in multilingual social media data. International Journal of Advanced Computer Science and Applications, 16(3), 324. https://doi.org/10.14569/IJACSA.2025.0160332
Antunes, F., Freire, M., Melo, P., & Costa, J. P. (2026). From emotional data to decisions: A systematic review on how airlines use sentiments and emotions to stay ahead. Journal of Air Transport Management, 131, 102911. https://doi.org/10.1016/j.jairtraman.2025.102911
Ashraf, S., & Choi, C. (2025). XP-STM: A cross-platform sentiment transferability model for negative public sentiment identification and mitigation. Journal of Engineering Research. https://doi.org/10.1016/j.jer.2025.12.005
Beshet, N. E., Salih, A. H., Salih, F. Z., Mahmood, H. E., Ahmed, A. A., Al Zahran, A., & Ghazal, T. M. (2026). Trends sentiment unveiled through deep dive into social media data. Procedia Computer Science, 275, 799–808. https://doi.org/10.1016/j.procs.2026.01.092
Kodati, D., & Tene, R. (2022). Identifying suicidal emotions on social media through transformer-based deep learning. Applied Intelligence, 53(10), 11885–11917. https://doi.org/10.1007/s10489-022-04060-8
Leon, M. (2025). Sentiment analysis: From rule-based lexicons to large language models. Intelligent Systems with Applications, 28, 200599. https://doi.org/10.1016/j.iswa.2025.200599
Maghsoudi, A., Nowakowski, S., Agrawal, R., Sharafkhaneh, A., Kunik, M. E., Naik, A. D., Xu, H., & Razjouyan, J. (2022). Sentiment analysis of insomnia-related tweets via a combination of transformers using Dempster-Shafer theory: Pre- and peri-COVID-19 pandemic retrospective study. Journal of Medical Internet Research, 24(12). https://doi.org/10.2196/41517
Md Suhaimin, M. S., Ahmad Hijazi, M. H., Moung, E. G., Nohuddin, P. N. E., Chua, S., & Coenen, F. (2023). Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions. Journal of King Saud University – Computer and Information Sciences, 35(9), 101776. https://doi.org/10.1016/j.jksuci.2023.101776
Oliveira, F. B., Haque, A., Mougouei, D., Evans, S., Sichman, J. S., & Singh, M. P. (2022). Investigating the emotional response to COVID-19 news on Twitter: A topic modelling and emotion classification approach. IEEE Access, 10, 16883–16897. https://doi.org/10.1109/ACCESS.2022.3150329
Parveen, S., Zaheen, U., & Khan, S. A. (2026). Deep learning approaches to sentiment analysis and text classification in social media data. The Critical Review of Social Sciences Studies, 4(1), 1168–1182. https://doi.org/10.59075/PVKFJC68
Sharma, U., Pandey, P., & Kumar, S. (2022). A transformer-based model for evaluation of information relevance in online social media: A case study of COVID-19 media posts. New Generation Computing, 40(4), 1029–1052. https://doi.org/10.1007/s00354-021-00151-1
Tabinda Kokab, S., Asghar, S., & Naz, S. (2022). Transformer-based deep learning models for the sentiment analysis of social media data. Array, 14, 100157. https://doi.org/10.1016/j.array.2022.100157
Tiwari, D., & Nagpal, B. (2022). KEAHT: A knowledge-enriched attention-based hybrid transformer model for social sentiment analysis. New Generation Computing, 40(4), 1165–1202. https://doi.org/10.1007/s00354-022-00182-2
Veluswamy, A. S., A, N., M, S., D, Y., M, A., & V, M. (2025). Natural language processing for sentiment analysis in social media: Techniques and case studies. ITM Web of Conferences, 76, 05004. https://doi.org/10.1051/itmconf/20257605004
Yazdi, M. G., Rafieizadeh, H., & Tajasob, P. (2025). Sentiment analysis of the 2024 Paris Olympics using RoBERTa language model. International Journal of Event and Festival Management, 17(1), 133–160. https://doi.org/10.1108/IJEFM-01-2025-0007
Zhou, Y., Li, Z., Tu, Y., & Lev, B. (2025). Precise refutation of social media rumors through users' perspective: Crowd classification based on believability. Expert Systems with Applications, 268, 126107. https://doi.org/10.1016/j.eswa.2024.126107
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Copyright (c) 2026 Ferdi Antonio, Handry Eldo, Arrazy Elba Ridha, Iwan Adhicandra, Cut Susan Octiva
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