An Ensemble-based Model for Sentiment Analysis of Kurdish Tweets

Thousands of comments are generated daily on social media in the Kurdistan Region. Sentiment analysis (SA) of these comments is valuable for organizations. The Kurdish language has three main dialects: Sorani (Central), Northern, and Southern. This study focuses on Sorani SA, where existing methods have limited accuracy. The proposed ensemble combines diverse models to improve sentiment classification. Preprocessing and word embedding using Roberta is the first phase of the method. The second phase consists of four proposed models, namely K-nearest neighbor, support vector machine, multilayer perceptron long short-term memory (LSTM), and bidirectional-LSTM (Bi-LSTM), which are used as classifiers. Finally, the ensemble weighted averaging technique is utilized to generate the final classification. To evaluate the performance of the proposed model, a dataset including 24211 unbalanced Soran tweets is first used, and after balancing, the dataset is used. The Bi-LSTM model attained an accuracy of 89.87% on the balanced dataset, and the proposed ensemble method increased the accuracy to 91.76%, which is better than the established state-of-the-art methods of Kurdish SA.