@article{MDD9CB65B,
title = "Korean Movie Review Sentiment Analysis using Self-Attention and Contextualized Embedding",
journal = "Journal of KIISE, JOK",
year = "2019",
issn = "2383-630X",
doi = "10.5626/JOK.2019.46.9.901",
author = "Cheoneum Park,Dongheon Lee,Kihoon Kim,Changki Lee,Hyunki Kim",
keywords = "Self-Attention,RNN,Sentiment analysis,movie review,contextualized embedding",
abstract = "Sentiment analysis is the processing task that involves collecting and classifying opinions about a specific object. However, it is difficult to grasp the subjectivity of a person using natural language, so the existing sentimental word dictionaries or probabilistic models cannot solve such a task, but the development of deep learning made it possible to solve the task. Self-attention is a method of modeling a given input sequence by calculating the attention weight of the input sequence itself and constructing a context vector with a weighted sum. In the context, a high weight is calculated between words with similar meanings. In this paper, we propose a method using a modeling network with self-attention and pre-trained contextualized embedding to solve the sentiment analysis task. The experimental result shows an accuracy of 89.82%."
}