# Sentiment Analysis for Sinhala Language using Deep Learning Techniques

arXiv: https://arxiv.org/abs/2011.07280 · HTML: https://mlsanity.com/paper/2011.07280/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification](https://mlsanity.com/paper/2208.07864/) (2022)
- [Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts](https://mlsanity.com/paper/2112.00468/) (2021)
- [Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content](https://mlsanity.com/paper/2504.13545/) (2025)
- [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)
- [LMSpell: Spell Correction with Pre-Trained Language Models](https://mlsanity.com/paper/2512.05414/) (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)
- [HelaBERT: Enhancing Sinhala Language Understanding with Dual Pooling Classification Head](https://mlsanity.com/paper/2608.22922/) (2026)

## Abstract (arXiv)

Due to the high impact of the fast-evolving fields of machine learning and deep learning, Natural Language Processing (NLP) tasks have further obtained comprehensive performances for highly resourced languages such as English and Chinese. However Sinhala, which is an under-resourced language with a rich morphology, has not experienced these advancements. For sentiment analysis, there exists only two previous research with deep learning approaches, which focused only on document-level sentiment analysis for the binary case. They experimented with only three types of deep learning models. In contrast, this paper presents a much comprehensive study on the use of standard sequence models such as RNN, LSTM, Bi-LSTM, as well as more recent state-of-the-art models such as hierarchical attention hybrid neural networks, and capsule networks. Classification is done at document-level but with more granularity by considering POSITIVE, NEGATIVE, NEUTRAL, and CONFLICT classes. A data set of 15059 Sinhala news comments, annotated with these four classes and a corpus consists of 9.48 million tokens are publicly released. This is the largest sentiment annotated data set for Sinhala so far.