Enhancing Low-Resource Sentiment Analysis: A Transfer Learning Approach

Abstract

The identification and extraction of subjective information from text, known as sentiment analysis, has seen advancements in employing cross-lingual approaches. However, the effective implementation and evaluation of sentiment analysis systems necessitate language-specific data to account for diverse sociocultural and linguistic variations. This paper outlines the process of collecting and annotating a dataset for sentiment analysis in Central Kurdish. We investigate classical machine learning and neural network-based techniques for this purpose. Furthermore, we adopt a transfer learning approach to enhance the performance by leveraging pretrained models for data augmentation. Our results demonstrate that despite the challenging nature of the task, data augmentation contributes to achieving high F1 scores and accuracy.