Kurdish Language Sentiment Analysis: Problems and Challenges

Abstract

The increasing usage of blogs, social networks, and forums for sharing opinions on a certain topic has created vast amounts of internet data. Therefore, Sentiment Analysis has gained great popularity among researchers and industry for analyzing the polarity of users' opinions. In recent years, Sentiment Analysis has been applied to various languages using machine learning-approach, corpus-based approach, and deep learning techniques since it is beneficial for creating an effective recommender system. The Kurdish Language is an Indo-European language, one of the official languages in Iraq, and it is also widely used in Turkey, Iran, and Syria. Although the importance of this Language is spoken by over 40 million people, to the best of our knowledge, no research has been done regarding the challenges and problems of Kurdish sentiment analysis. Our research aims to highlight the latest studies and examine the most critical challenges of applying sentiment analysis approaches to the Kurdish Language. The study includes determining each challenge in each step of sentiment analysis processing in the Kurdish Language. In addition, our proposed methodology that could help address most of these challenges is implementing a hybrid approach by combining machine learning and lexicon-based approaches to improve the proficiency of sentiment classification in the Kurdish Language.

Enhancing Low-Resource Sentiment Analysis: A Transfer Learning Approach

“…Also, two different proposed methods, a machine learning-based method and a lexicon-based method, are presented to face these obstacles. Furthermore, Awlla and Veisi [25] address sentiment analysis for Kurdish and describe the creation of a dataset containing 14,881 comments from various Facebook pages. To create an analyzer, Word2vec embeddings, along with a recurrent neural network classifier, are used with a reported accuracy of 71.35%.…”