# Transfer Learning for Low-Resource Sentiment Analysis

arXiv: https://arxiv.org/abs/2304.04703 · HTML: https://mlsanity.com/paper/2304.04703/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages](https://mlsanity.com/paper/2205.15960/) (2022)
- [To BAN or not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection](https://mlsanity.com/paper/2007.05304/) (2020)
- [Data augmentation for low resource sentiment analysis using generative adversarial networks](https://mlsanity.com/paper/1902.06818/) (2019)
- [Towards Machine Translation for the Kurdish Language](https://mlsanity.com/paper/2010.06041/) (2020)
- [Sentiment Analysis on YouTube: A Brief Survey](https://mlsanity.com/paper/1511.09142/) (2015)
- [Semi-supervised and Transfer learning approaches for low resource sentiment classification](https://mlsanity.com/paper/1806.02863/) (2018)

## Built on by (most cited first, 1 of 1)

- [KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis](https://mlsanity.com/paper/2509.16804/) (2025)

## Abstract (arXiv)

Sentiment analysis is the process of identifying and extracting subjective information from text. Despite the advances to employ cross-lingual approaches in an automatic way, the implementation and evaluation of sentiment analysis systems require language-specific data to consider various sociocultural and linguistic peculiarities. In this paper, the collection and annotation of a dataset are described for sentiment analysis of Central Kurdish. We explore a few classical machine learning and neural network-based techniques for this task. Additionally, we employ an approach in transfer learning to leverage pretrained models for data augmentation. We demonstrate that data augmentation achieves a high F1 score and accuracy despite the difficulty of the task.