  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 F$_1$ score and accuracy despite the difficulty of the task.
