합성곱 신경망을 이용한 음절 피처맵 생성 및 감성 분석

Abstract

Sentiment analysis is a technique for analyzing subjective attitudes, opinions, and emotions of people in a text. When conducting sentiment analysis understanding the structure of the language used in the text is very important. In this paper, we noted the characteristics of the Korean language that a syllable consists of three elements: Initial sound, Intermediate sound, Final sound. Thus, we compare sentiment classification models that can reflect the characteristics. These models, which expresses syllables by combination of initial sound, intermediate sound, final sound. One of them is improved in classification accuracy over the existing character-level model. But not only that, This model is robust to the misspelled word compared to Syllable-level model and Morph-level model because it uses a character-level representation of a sentence as input.