GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set
This provides a valuable annotated dataset for Sinhala Natural Language Processing and music emotion recognition, though it is domain-specific and incremental.
The researchers created GeeSanBhava, a manually annotated dataset of Sinhala YouTube music comments using Russell's Valence-Arousal model, achieving high inter-annotator agreement (Fleiss kappa = 84.96%) and distinct emotional profiles for different songs. An optimized Multi-Layer Perceptron model achieved a ROC-AUC score of 0.887 on this dataset.
This study introduce GeeSanBhava, a high-quality data set of Sinhala song comments extracted from YouTube manually tagged using Russells Valence-Arousal model by three independent human annotators. The human annotators achieve a substantial inter-annotator agreement (Fleiss 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.