한글 음소 단위 딥러닝 모형을 이용한 감성분석

• 영문명: Sentiment Analysis Using Deep Learning Model based on Phoneme-level Korean
• 발행기관: 한국IT서비스학회
• 저자명: 이재준(Jae Jun Lee) 권순범(Suhn Beom Kwon) 안성만(Sung Mahn Ahn)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제17권 제1호, 79~89쪽, 전체 11쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2018.02.28

영문 초록

Sentiment analysis is a technique of text mining that extracts feelings of the person who wrote the sentence like movie review. The preliminary researches of sentiment analysis identify sentiments by using the dictionary which contains negative and positive words collected in advance. As researches on deep learning are actively carried out, sentiment analysis using deep learning model with morpheme or word unit has been done. However, this model has disadvantages in that the word dictionary varies according to the domain and the number of morphemes or words gets relatively larger than that of phonemes. Therefore, the size of the dictionary becomes large and the complexity of the model increases accordingly. We construct a sentiment analysis model using recurrent neural network by dividing input data into phoneme-level which is smaller than morpheme-level. To verify the performance, we use 30,000 movie reviews from the Korean biggest portal, Naver. Morpheme-level sentiment analysis model is also implemented and compared. As a result, the phonemelevel sentiment analysis model is superior to that of the morpheme-level, and in particular, the phoneme-level model using LSTM performs better than that of using GRU model. It is expected that Korean text processing based on a phoneme-level model can be applied to various text mining and language models.

목차

1. 서 론

2. 관련 연구

3. 순환신경망을 이용한 음소 단위 감성분석모형

4. 실 험

5. 결 론

키워드

Sentiment Analysis Deep Learning Sequential Model Phoneme Unit LSTM GRU