딥러닝 기반의 다범주 감성분석 모델 개발

• 영문명: Development of Deep Learning Models for Multi-class Sentiment Analysis
• 발행기관: 한국IT서비스학회
• 저자명: 알렉스 샤이코니(M. Alex Syaekhoni) 서상현(Sang Hyun Seo) 권영식(Young S. Kwon)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제16권 제4호, 149~160쪽, 전체 12쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2017.12.31

영문 초록

Sentiment analysis is the process of determining whether a piece of document, text or conversation is positive, negative, neural or other emotion. Sentiment analysis has been applied for several real-world applications, such as chatbot. In the last five years, the practical use of the chatbot has been prevailing in many field of industry. In the chatbot applications, to recognize the user emotion, sentiment analysis must be performed in advance in order to understand the intent of speakers. The specific emotion is more than describing positive or negative sentences. In light of this context, we propose deep learning models for conducting multi-class sentiment analysis for identifying speaker’s emotion which is categorized to be joy, fear, guilt, sad, shame, disgust, and anger. Thus, we develop convolutional neural network (CNN), long short term memory (LSTM), and multi-layer neural network models, as deep neural networks models, for detecting emotion in a sentence. In addition, word embedding process was also applied in our research. In our experiments, we have found that long short term memory (LSTM) model performs best compared to convolutional neural networks and multi-layer neural networks. Moreover, we also show the practical applicability of the deep learning models to the sentiment analysis for chatbot.

목차

1. 서 론

2. 관련 연구

3. 딥러닝 기반 감성분석 모델의 제안

4. 실험 결과

5. 결론과 향후연구

키워드

Sentiment Analysis Convolutional Neural Networks Long Short-Term Memory Word2vec