KurdiSent: a corpus for kurdish sentiment analysis

Abstract

Language is essential for communication and the expression of feelings and sentiments. As technology advances, language has become increasingly ubiquitous in our lives. One of the most critical research areas in natural language processing (NLP) is sentiment analysis, which aims to identify and extract opinions and attitudes from text. Sentiment analysis is particularly useful for understanding public opinion on products, services, and topics of interest. While sentiment analysis systems are well-developed for English, this differs for other languages, such as Kurdish. This is because less-resourced languages have fewer NLP resources, including annotated datasets. To bridge this gap, this paper introduces KurdiSent, the first manually annotated dataset for Kurdish sentiment analysis. KurdiSent consists of over 12,000 instances labeled as positive, negative, or neutral. The corpus covers the Sorani dialect of Kurdish, the most widely spoken dialect. To ensure the quality of KurdiSent, the dataset was trained on machine learning and deep learning classifiers. The experimental results indicated that XLM-R outperformed all machine learning and deep learning classifiers, with an accuracy of 85%, compared to 81% for the best machine learning classifier. KurdiSent is a valuable resource for the NLP community, as it will enable researchers to develop and improve sentiment analysis systems for Kurdish. The corpus will facilitate a better understanding of public opinion in Kurdish-speaking communities.