- Volume 23 Issue 3
DOI QR Code
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)
- Received : 2018.05.18
- Accepted : 2018.06.27
- Published : 2018.06.30
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.
Supported by : National Research Foundation of Korea
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