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.
@article{arxiv.2304.04703,
title = {Transfer Learning for Low-Resource Sentiment Analysis},
author = {Razhan Hameed and Sina Ahmadi and Fatemeh Daneshfar},
journal= {arXiv preprint arXiv:2304.04703},
year = {2023}
}