# Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts

arXiv: https://arxiv.org/abs/2112.00468 · HTML: https://mlsanity.com/paper/2112.00468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Distant supervision for emotion detection using Facebook reactions](https://mlsanity.com/paper/1611.02988/) (2016)
- [Sentiment Analysis for Sinhala Language using Deep Learning Techniques](https://mlsanity.com/paper/2011.07280/) (2020)
- [Sinhala Language Corpora and Stopwords from a Decade of Sri Lankan Facebook](https://mlsanity.com/paper/2007.07884/) (2020)
- [Shared Feelings: Understanding Facebook Reactions to Scholarly Articles](https://mlsanity.com/paper/1905.10975/) (2019)
- [Jointly Learning to Detect Emotions and Predict Facebook Reactions](https://mlsanity.com/paper/1909.10779/) (2019)
- [Facebook Reaction-Based Emotion Classifier as Cue for Sarcasm Detection](https://mlsanity.com/paper/1805.06510/) (2018)
- [Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning](https://mlsanity.com/paper/1810.01912/) (2018)

## Built on by (most cited first, 3 of 3)

- [Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data](https://mlsanity.com/paper/2201.03941/) (2022)
- [Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages](https://mlsanity.com/paper/2210.14472/) (2022)
- [Fault of Our Stars: Behavioral Drivers of Rating-Sentiment Incongruence](https://mlsanity.com/paper/2606.25518/) (2026)

## Abstract (arXiv)

The Facebook network allows its users to record their reactions to text via a typology of emotions. This network, taken at scale, is therefore a prime data set of annotated sentiment data. This paper uses millions of such reactions, derived from a decade worth of Facebook post data centred around a Sri Lankan context, to model an eye of the beholder approach to sentiment detection for online Sinhala textual content. Three different sentiment analysis models are built, taking into account a limited subset of reactions, all reactions, and another that derives a positive/negative star rating value. The efficacy of these models in capturing the reactions of the observers are then computed and discussed. The analysis reveals that binary classification of reactions, for Sinhala content, is significantly more accurate than the other approaches. Furthermore, the inclusion of the like reaction hinders the capability of accurately predicting other reactions.