루간다어 감성 분류를 위한 저자원 유튜브 댓글 인코딩

Abstract

The recent boom in social networks usage has generated some multilingual opinion data for low-resource languages. Luganda is one of the major languages in Uganda, thus it is a low-resource language and Luganda corpora for sentiment analysis especially for YouTube is not easily available. In this paper, we propose assumptions to guide collection of Luganda comments using Luganda YouTube video opinions for sentiment analysis. We evaluate the suitability of our clean YouTube comments (158) dataset for sentiment analysis using selected machine learning and deep learning classification algorithms. Given the low-resource setting, the dataset performs best with Gaussian Naive Bayes for machine learning (55%) and deep learning Multilayer Perceptron sequential model scoring (68.8%) when dataset splitting is at 10% for test set with Luganda comment segmentation.