THE EMOTIONAL ANALYSIS OF SONG LYRICS AND VIDEO COMMENTS ON YOUTUBE USING DEEP LEARNING

Authors

DOI:

https://doi.org/10.33480/jitk.v11i4.7321

Keywords:

DistilRoBERTa, Emotion Classification, LSTM, Lyrics, YouTube Comments

Abstract

Digital media platforms shape public perception of music through song lyrics and audience comments. This study analyzes emotions expressed in the lyrics and YouTube comments of Taylor Swift’s “Fortnight” using deep learning models. The dataset consists of 42 lyric lines and 13,406 user comments collected from April to December 2024. Emotion labeling was manually performed based on Plutchik’s eight basic emotions with an additional neutral category. This research applies two models: Long Short-Term Memory (LSTM) and DistilRoBERTa, with random oversampling to address class imbalance. Performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. As a single-song case study, this research provides a focused comparison of sequential and transformer-based architectures for simultaneous emotion analysis of lyrics and audience responses. The results show that DistilRoBERTa achieved higher accuracy (94.07%) than LSTM (90.75%), indicating the advantage of contextual transformer models in capturing nuanced emotional expressions within this dataset. However, the findings are limited to the thematic characteristics of this single-song dataset and should be interpreted within this contextual scope.

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References

[1] H. Zhao, P. Ru, and H. Jia, “TikTok Users Migration to Xiaohongshu (Rednote): Emotional Dynamics, Platform Governance, and an NCA-SEM Analysis in Cross-Cultural Adaptation,” Int. J. Hum. Comput. Interact., vol. 42, no. 4, pp. 2787 – 2812, 2026, doi: 10.1080/10447318.2025.2530088.

[2] C.-J. Wang, X. Zhang, Z. Gou, and Y. Wu, “Yesterday once more: collective storytelling and public engagement with digital cultural products on the music streaming platform,” Humanit. Soc. Sci. Commun., vol. 11, no. 1, 2024, doi: 10.1057/s41599-024-03636-8.

[3] P. Nandwani and R. Verma, “A review on sentiment analysis and emotion detection from text,” Soc. Netw. Anal. Min., vol. 11, 2021, doi: 10.1007/s13278-021-00776-6.

[4] F. A. Acheampong, H. Nunoo-Mensah, and W. Chen, “Transformer models for text-based emotion detection: a review of BERT-based approaches,” Artif. Intell. Rev., vol. 54, no. 8, pp. 5789–5829, Dec. 2021, doi: 10.1007/s10462-021-09958-2.

[5] T. Chutia and N. Baruah, “A review on emotion detection by using deep learning techniques,” Artif. Intell. Rev., vol. 57, no. 8, Aug. 2024, doi: 10.1007/s10462-024-10831-1.

[6] S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning–based Text Classification: A Comprehensive Review,” ACM Comput. Surv., vol. 54, no. 3, Apr. 2021, doi: 10.1145/3439726.

[7] R. Huang, “Comparative Study of LSTM and Transformer Models for Sentiment Analysis on the IMDB 5000 Dataset,” in 2025 4th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID), 2025, pp. 209–218. doi: 10.1109/ICAID65275.2025.11034482.

[8] A. Alhadlaq and A. Altheneyan, “Distilroberta2gnn: a new hybrid deep learning approach for aspect-based sentiment analysis,” PeerJ Comput. Sci., vol. 10, 2024, doi: 10.7717/PEERJ-CS.2267.

[9] R. Olusegun, T. Oladunni, H. Audu, Y. A. O. Houkpati, and S. Bengesi, “Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach,” IEEE Access, vol. 11, pp. 49882–49894, 2023, doi: 10.1109/ACCESS.2023.3277868.

[10] T. Shaikh and A. Jadhav, “Leveraging Deep Learning and NLP for Data Analysis and Classification,” in 2025 International Conference on Applications of Machine Intelligence and Data Analytics, ICAMIDA 2025, 2025. doi: 10.1109/ICAMIDA64673.2025.11209701.

[11] S. Chaudhary, P. Kapoor, J. Rani, A. K. Kulandasamy, R. Shobana, and T. Jadhav, “NLP-BASED MUSIC LYRIC ANALYSIS IN EDUCATION,” ShodhKosh: Journal of Visual and Performing Arts, vol. 6, no. 5s, pp. 239 – 249, 2025, doi: 10.29121/shodhkosh.v6.i5s.2025.6884.

[12] K. Takiguchi, M. Nakahara, and K. Sakamoto, “Influence of Comment Sentiment on YouTube Subscribers and View Counts by Genre,” in Proceedings of The 2026 International Conference on Artificial Life and Robotics, 2026.

[13] S. S. Maw, E. C. Lwin, W. Mar, N. S. Paw, M. M. Khaing, and T. T. Aung, “Sentiment Analysis with YouTube Comments Using Deep Learning Approaches,” in Proceedings of the 21st IEEE International Conference on Computer Applications 2024, ICCA 2024, 2024, pp. 9 – 15. doi: 10.1109/ICCA62361.2024.10532851.

[14] C. Travanca, M. Cruz, and A. Oliveira, “Emotion in Words: The Role of Ed Sheeran and Sia’s Lyrics on the Musical Experience,” Computers, vol. 14, no. 11, 2025, doi: 10.3390/computers14110460.

[15] A. Jabbar, S. Iqbal, M. I. Tamimy, A. Rehman, S. A. Bahaj, and T. Saba, “An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems,” IEEE Access, vol. 11, pp. 133681–133702, 2023, doi: 10.1109/ACCESS.2023.3332710.

[16] V. H. Pranatawijaya, N. N. K. Sari, R. A. Rahman, E. Christian, and S. Geges, “Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews,” Journal of Information Systems Engineering and Business Intelligence, vol. 10, no. 3, pp. 328–339, Oct. 2024, doi: 10.20473/jisebi.10.3.328-339.

[17] S. Chakraborty, P. Das, S. Mahmud Dipto, M. A. Pramanik, and J. Noor, “An Analytical Review of Preprocessing Techniques in Bengali Natural Language Processing,” IEEE Access, vol. 13, pp. 112428–112445, 2025, doi: 10.1109/ACCESS.2025.3574234.

[18] M. U. Albab, Y. K. P., and M. N. Fawaiq, “Optimization of the Stemming Technique on Text Preprocessing President 3 Periods Topic,” Jurnal Transformatika, vol. 20, no. 2, pp. 1–12, Sep. 2023, doi: 10.26623/transformatika.v20i2.5374.

[19] A. Jabbar, S. Iqbal, M. I. Tamimy, A. Rehman, S. A. Bahaj, and T. Saba, “An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems,” IEEE Access, vol. 11, pp. 133681–133702, 2023, doi: 10.1109/ACCESS.2023.3332710.

[20] A. Koufakou and E. Nieves, “Emotion-Annotated Data in NLP: Perspectives on Recent Resources and Practices,” in Eighth Widening NLP Workshop (WiNLP 2024) Phase II, 2024. [Online]. Available: https://openreview.net/forum?id=TTEBCqtfTm

[21] H. Guerdelli, C. Ferrari, J. B. Cardia Neto, S. Berretti, W. Barhoumi, and A. Del Bimbo, “Towards a Better Understanding of Human Emotions: Challenges of Dataset Labeling,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14365 LNCS, pp. 242 – 254, 2024, doi: 10.1007/978-3-031-51023-6_21.

[22] D. Edmonds and J. Sedoc, “Multi-Emotion Classification for Song Lyrics,” in Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Online: Association for Computational Linguistics, Apr. 2021, pp. 221–235. [Online]. Available: https://aclanthology.org/2021.wassa-1.24/

[23] N. Raghunathan and K. Saravanakumar, “Challenges and Issues in Sentiment Analysis: A Comprehensive Survey,” IEEE Access, vol. 11, pp. 69626–69642, 2023, doi: 10.1109/ACCESS.2023.3293041.

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Published

2026-05-26

Issue

Vol. 11 No. 4 (2026): JITK Issue May 2026

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Articles

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Copyright (c) 2026 Nova Noor Kamala Sari, Widiatry Widiatry, Inda Fitria Maharisty

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
“THE EMOTIONAL ANALYSIS OF SONG LYRICS AND VIDEO COMMENTS ON YOUTUBE USING DEEP LEARNING”, jitk, vol. 11, no. 4, pp. 1253–1261, May 2026, doi: 10.33480/jitk.v11i4.7321.