Central Kurdish Sentiment Analysis Using Deep Learning

Abstract

Sentiment Analysis (SA) as a type of opinion mining and as a more general topic than polarity detection, is widely used for analyzing user's reviews or comments of online expressions, which is implemented using various techniques among which the Artificial Neural Network (ANN) is the most popular one. This paper addresses the development of an SA system for the Central Kurdish language (CKB) using deep learning. Increasing the efficiency and strengthening of the SA system relies on a robust language model. In addition, for creating and training a robust language model, collecting a large amount of text corpus is required and we have created a corpus of size 300 million tokens for CKB. Also, to train the SA model, we collected 14,881 comments on Facebook, then they are labeled manually. The combination of Word2Vec for the language model and Long Short-Term Memory (LSTM) for the classifier are used to create an SA model on the CKB SA dataset. These deep learning-based techniques are the most well-known methods in this field which have received high performance in SA for various languages. The performance of the proposed method for 3 classes SA is %71.35 accuracy. This result is superior to the best-reported result for CKB.

Keywords

• sentiment analysis
• deep learning
• word2vec
• lstm
• central kurdish language