Korean Movie Review Sentiment Analysis using Self-Attention and Contextualized Embedding

Cheoneum Park, Dongheon Lee, Kihoon Kim, Changki Lee, Hyunki Kim

http://doi.org/10.5626/JOK.2019.46.9.901

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%.