# Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data

arXiv: https://arxiv.org/abs/2201.03941 · HTML: https://mlsanity.com/paper/2201.03941/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Survey on Publicly Available Sinhala Natural Language Processing Tools and Research](https://mlsanity.com/paper/1906.02358/) (2019)
- [Investigating Capsule Networks with Dynamic Routing for Text Classification](https://mlsanity.com/paper/1804.00538/) (2018)
- [Hierarchical Attentional Hybrid Neural Networks for Document Classification](https://mlsanity.com/paper/1901.06610/) (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)
- [Measuring the Diversity of Facebook Reactions to Research](https://mlsanity.com/paper/2001.01029/) (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)
- [Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts](https://mlsanity.com/paper/2112.00468/) (2021)

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

- [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 relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04%. Further, since all the deep learning models show F1 scores above 75% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.