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http://dx.doi.org/10.9723/jksiis.2018.23.3.059

Korean and English Sentiment Analysis Using the Deep Learning  

Ramadhani, Adyan Marendra (Department of Management Information Systems, Dong-A University)
Choi, Hyung Rim (Department of Management Information Systems, Dong-A University)
Lim, Seong Bae (Department of Management Information Systems, St. Mary's University)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.23, no.3, 2018 , pp. 59-71 More about this Journal
Abstract
Social media has immense popularity among all services today. Data from social network services (SNSs) can be used for various objectives, such as text prediction or sentiment analysis. There is a great deal of Korean and English data on social media that can be used for sentiment analysis, but handling such huge amounts of unstructured data presents a difficult task. Machine learning is needed to handle such huge amounts of data. This research focuses on predicting Korean and English sentiment using deep forward neural network with a deep learning architecture and compares it with other methods, such as LDA MLP and GENSIM, using logistic regression. The research findings indicate an approximately 75% accuracy rate when predicting sentiments using DNN, with a latent Dirichelet allocation (LDA) prediction accuracy rate of approximately 81%, with the corpus being approximately 64% accurate between English and Korean.
Keywords
English Text; Korean Text; Sentiment; Deep Learning; Neural Network; Deep Neural Network;
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Times Cited By KSCI : 2  (Citation Analysis)
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