This study introduces GeeSanBhava, a high-quality data set of Sinhala song comments extracted from YouTube manually tagged using RussellâÂÂs Valence-Arousal model by three independent human annotators. The human annotators achieve a substantial inter-annotator agreement (Fleissâ kappa = 84.96\%). The analysis revealed distinct emotional profiles for different songs, highlighting the importance of comment-based emotion mapping. The study also addressed the challenges of comparing comment-based and song-based emotions, mitigating biases inherent in user-generated content. A number of Machine learning and deep learning models were pre-trained on a related large data set of Sinhala News comments in order to report the zero-shot result of our Sinhala YouTube comment data set. An optimized Multi-Layer Perceptron model, after extensive hyperparameter tuning, achieved a ROC-AUC score of 0.887. The model is a three-layer MLP with a configuration of 256, 128, and 64 neurons. This research contributes a valuable annotated dataset and provides insights for future work in Sinhala Natural Language Processing and music emotion recognition. The complete data set is publicly available at: https://bit.ly/SinhalaYoutubeComments.
Keywords: Sinhala | Natural Language Processing | Machine Learning / Deep Learning | Emotion Recognition | Sentiment Analysis |
@inproceedings{de2025geesanbhava,
title={{GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set}},
author={De Mel, Yomal and de Silva, Nisansa},
booktitle={International Conference on Computational Collective Intelligence},
pages={157--171},
year={2025},
organization={Springer},
abstract={This study introduces GeeSanBhava, a high-quality data set of Sinhala song comments extracted from YouTube manually tagged using RussellâÂÂs Valence-Arousal model by three independent human annotators. The human annotators achieve a substantial inter-annotator agreement (Fleissâ kappa = 84.96\%). The analysis revealed distinct emotional profiles for different songs, highlighting the importance of comment-based emotion mapping. The study also addressed the challenges of comparing comment-based and song-based emotions, mitigating biases inherent in user-generated content. A number of Machine learning and deep learning models were pre-trained on a related large data set of Sinhala News comments in order to report the zero-shot result of our Sinhala YouTube comment data set. An optimized Multi-Layer Perceptron model, after extensive hyperparameter tuning, achieved a ROC-AUC score of 0.887. The model is a three-layer MLP with a configuration of 256, 128, and 64 neurons. This research contributes a valuable annotated dataset and provides insights for future work in Sinhala Natural Language Processing and music emotion recognition. The complete data set is publicly available at: https://bit.ly/SinhalaYoutubeComments.},
doi={10.1007/978-3-032-10209-6_11},
misc={https://link.springer.com/chapter/10.1007/978-3-032-10209-6_11,SIN:NLP:ML,https://goo.gl/iY6aTr}
}
ActivePI: Nisansa de Silva
We propose to advance social media text analysis by combining linguistic study, preprocessing, and deep learning models to better capture sentiment, structure, and meaning in informal, large-scale user-generated text.