# Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study

arXiv: https://arxiv.org/abs/2501.18633 · HTML: https://mlsanity.com/paper/2501.18633/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 28 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification](https://mlsanity.com/paper/2208.07864/) (2022)
- [Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook](https://mlsanity.com/paper/2007.07884/) (2020)
- [Transformer-based approach towards music emotion recognition from lyrics](https://mlsanity.com/paper/2101.02051/) (2021)
- [NSINA: A News Corpus for Sinhala](https://mlsanity.com/paper/2403.16571/) (2024)
- [Text-based Sentiment Analysis and Music Emotion Recognition](https://mlsanity.com/paper/1810.03031/) (2018)

## Built on by (most cited first, 2 of 2)

- [GeeSanBhava: Sentiment Tagged Sinhala Music Video Comment Data Set](https://mlsanity.com/paper/2511.18146/) (2025)
- [Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation](https://mlsanity.com/paper/2607.05260/) (2026)

## Abstract (arXiv)

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