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루간다어 감성 분류를 위한 저자원 유튜브 댓글 인코딩Low-resource YouTube comment encoding for Luganda sentiment classification performance

Other Titles
Low-resource YouTube comment encoding for Luganda sentiment classification performance
Authors
Abdul Male Ssentumbwe정유철이현아김병만
Issue Date
May-2020
Publisher
한국디지털콘텐츠학회
Keywords
Luganda; Low-resource language; Sentiment Analysis; YouTube Comments; Opinion Mining; Luganda; 저자원 언어; 감성분석; 유튜브 댓글; 의견 마이닝
Citation
디지털콘텐츠학회논문지, v.21, no.5, pp.951 - 958
Journal Title
디지털콘텐츠학회논문지
Volume
21
Number
5
Start Page
951
End Page
958
URI
https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20275
DOI
10.9728/dcs.2020.21.5.951
ISSN
1598-2009
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.
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Department of Computer Engineering > 1. Journal Articles
Department of Computer Software Engineering > 1. Journal Articles

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