Visualisasi Interaktif Emosi pada Teks Uji Terkontrol Menggunakan RoBERTa GoEmotions dan Roda Emosi Plutchik
Abstract
Text-based emotion classification results are commonly presented as labels, tables, or probability scores, making relationships among emotions difficult for users to interpret intuitively. This problem indicates the need for a visualization approach that not only presents the dominant emotion but also shows the intensity and relationship of supporting emotions in a single visual representation. This study aims to develop an interactive emotion visualization system for controlled test texts by integrating the RoBERTa GoEmotions model and the Plutchik Emotion Wheel. The system processes input text through light preprocessing, Transformer model inference, rule based emotion mapping into the eight basic Plutchik emotions, max normalization, visual threshold filtering, and radial visualization using D3.js. The contribution of this study lies in integrating the more detailed GoEmotions output into the basic emotion structure of Plutchik, applying normalization and visual threshold filtering to improve readability, and developing an interactive visualization that supports the interpretation of text emotion analysis results. Functional testing was conducted to examine the consistency of the inference process, emotion mapping, score normalization, and visualization generation. The test example shows that a sentence expressing success produces joy as the dominant emotion, with an aggregate score of 1.220 and a normalized value of 1.000. This result is consistent with the context of the input text and is visualized as the most dominant petal in the Plutchik Emotion Wheel. The findings indicate that integrating modern NLP models with interactive visualization can improve the interpretability of text-based emotion analysis compared with numerical or tabular presentation.
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References
Babaali, B., & Shayaninasab, M. (2024). Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers. ArXiv.
Creanga, C., & Dinu, L. P. (2024). ISDS-NLP at SemEval-2024 Task 10: Transformer based neural networks for emotion recognition in conversations. Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), 3, 649–654.
Dong, S., Fan, X., & Ma, X. (2024). Multichannel Multimodal Emotion Analysis of Cross-Modal Feedback Interactions Based on Knowledge Graph. Neural Processing Letters, 56(3), 1–17. https://doi.org/10.1007/s11063-024-11641-w
Feng, Y., & Wei, R. (2024). Method of Multi-Label Visual Emotion Recognition Fusing Fore-Background Features. Applied Sciences, 14(8564). https://doi.org/10.3390/app14188564
Gamage, G., Silva, D. De, Mills, N., Alahakoon, D., & Manic, M. (2024). Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi‑granular emotion analysis. Journal of Big Data. https://doi.org/10.1186/s40537-024-00953-2
Kastrati, M., Kastrati, Z., Imran, A. S., & Marenglen, B. (2024). Leveraging distant supervision and deep learning for twitter sentiment and emotion classification. Journal of Intelligent Information Systems, 62, 1045–1070. https://doi.org/10.1007/s10844-024-00845-0
Lim, D., & Cheong, Y.-G. (2024). Integrating Plutchik ’ s Theory with Mixture of Experts for Enhancing Emotion Classification. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 857–867.
Liu, R., Chao, Y., Ma, X., Sha, X., Sun, L., Li, S., & Chang, S. (2024). ERTNet: an interpretable transformer-based framework for EEG emotion recognition. Frontiers in Neuroscience, 18(1320645), 1–11. https://doi.org/10.3389/fnins.2024.1320645
Maruf, A. Al, Khanam, F., Haque, M., Jiyad, Z. M., Mridha, M. F., & Aung, Z. (2024). Challenges and Opportunities of Text-Based Emotion Detection: A Survey. IEEE Access, 12(January), 18416–18450. https://doi.org/10.1109/ACCESS.2024.3356357
Min, S., Yang, J., Lim, S., Lee, J., Lee, S., & Lim, S. (2024). Emotion Recognition Using Transformers with Masked Learning. ArXiv.
Nfaoui, E. H., & Elfaik, H. (2024). Evaluating Arabic Emotion Recognition Task Using ChatGPT Models: A Comparative Analysis between Emotional Stimuli Prompt, Fine-Tuning, and In-Context Learning. Journal of Theoretical and Applied Electronic Commerce Research, 19, 1118–1141. https://doi.org/10.3390/jtaer19020058
Olusegun, R., Oladunni, T., Audu, H., Houkpati, Y. A. O., & Bengesi, S. (2023). Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing ( NLP ) Approach. IEEE Access, 11(May), 49882–49894. https://doi.org/10.1109/ACCESS.2023.3277868
Plaza-del-Arco, F. M., Curry, A., Curry, A. C., & Hovy, D. (2024). Emotion Analysis in NLP : Trends, Gaps and Roadmap for Future Directions. LREC-COLING 2024, 5696–5710.
Plisiecki, H., & Sobieszek, A. (2024). Emotion topology: extracting fundamental components of emotions from text using word embeddings. Frontiers in Psychology, 15(1401084). https://doi.org/10.3389/fpsyg.2024.1401084
Semeraro, A., Vilella, S., & Ruffo, G. (2021). PyPlutchik: Visualising and comparing emotion-annotated corpora. PLOS ONE, 16(9), 1–24. https://doi.org/10.1371/journal.pone.0256503
Sitoula, R. S., Pramanik, M., & Panigrahi, R. (2024). JSCDM Fine-Grained Classification for Emotion Detection Using Advanced Neural Models and GoEmotions Dataset. Journal of Soft Computing and Data Mining, 5(2), 62–71. https://doi.org/10.30880/jscdm.2024.05.02.005
Tian, Y., Wang, Z., Chen, D., & Yao, H. (2024). TriCAFFNet: A Tri-Cross-Attention Transformer with a Multi-Feature Fusion Network for Facial Expression Recognition. Sensors, 24(5391). https://doi.org/10.3390/s24165391
Younis, E. M. G., Mohsen, S., Houssein, E. H., & Ibrahim, O. A. S. (2024). Machine learning for human emotion recognition: a comprehensive review. In Neural Computing and Applications (Vol. 36, Issue 16). Springer London. https://doi.org/10.1007/s00521-024-09426-2
Zhang, Q., Wang, Z., Zhang, D., Niu, W., Caldwell, S., Gedeon, T., Liu, Y., & Qin, Z. (2024). Visual Prompting in LLMs for Enhancing Emotion Recognition. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 4484–4499.
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