This research investigates the area of Music Information Retrieval (MIR) and Music Emotion Recognition (MER) in relation to Sinhala songs, an underexplored field in music studies. The purpose of this study is to analyze the behavior of Sinhala comments on YouTube Sinhala song videos using social media comments as primary data sources. These included comments from 27 YouTube videos containing 20 different Sinhala songs, which were carefully selected so that strict linguistic reliability would be maintained and relevancy ensured. This process led to a total of 93,116 comments being gathered upon which the dataset was refined further by advanced filtering methods and transliteration mechanisms resulting into 63,471 Sinhala comments. Additionally, 964 stop-words specific for the Sinhala language were algorithmically derived out of which 182 matched exactly with English stop-words from NLTK corpus once translated. Also, comparisons were made between general domain corpora in Sinhala against the YouTube Comment Corpus in Sinhala confirming latter as good representation of general domain. The meticulously curated data set as well as the derived stop-words form important resources for future research in the fields of MIR and MER, since they could be used and demonstrate that there are possibilities with computational techniques to solve complex musical experiences across varied cultural traditions.
Keywords: Natural Language Processing | Sinhala | Information Retrieval | Emotion Recognition | Sentiment Analysis | Semantic Analysis | Social Media |
@article{de2025linguistic,
title={{Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study}},
author={De Mel, W M Yomal and de Silva, Nisansa},
journal={ICTer},
pages={121-130},
year={2025},
abstract={This research investigates the area of Music Information Retrieval (MIR) and Music Emotion Recognition (MER) in relation to Sinhala songs, an underexplored field in music studies. The purpose of this study is to analyze the behavior of Sinhala comments on YouTube Sinhala song videos using social media comments as primary data sources. These included comments from 27 YouTube videos containing 20 different Sinhala songs, which were carefully selected so that strict linguistic reliability would be maintained and relevancy ensured. This process led to a total of 93,116 comments being gathered upon which the dataset was refined further by advanced filtering methods and transliteration mechanisms resulting into 63,471 Sinhala comments. Additionally, 964 stop-words specific for the Sinhala language were algorithmically derived out of which 182 matched exactly with English stop-words from NLTK corpus once translated. Also, comparisons were made between general domain corpora in Sinhala against the YouTube Comment Corpus in Sinhala confirming latter as good representation of general domain. The meticulously curated data set as well as the derived stop-words form important resources for future research in the fields of MIR and MER, since they could be used and demonstrate that there are possibilities with computational techniques to solve complex musical experiences across varied cultural traditions.},
doi={10.4038/icter.v18i2.7299},
volume={18},
number={2},
misc={https://arxiv.org/abs/2501.18633,NLP:SIN,https://goo.gl/iY6aTr}
}
ActivePI: Nisansa de Silva
We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.
ActivePI: Nisansa de Silva
We propose to build and analyze Sinhala language resources, datasets, and models to strengthen NLP research for this low-resource language, while connecting it to English and Tamil through bilingual lexicons, embeddings, and cross-lingual methods.
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.