부도예측 모형에서 뉴스 분류를 통한 효과적인 감성분석에 관한 연구

• 영문명: A Study on Effective Sentiment Analysis through News Classification in Bankruptcy Prediction Model
• 발행기관: 한국IT서비스학회
• 저자명: 김찬송() 신민수()
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제18권 제1호, 187~200쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2019.03.30

영문 초록

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

목차

1. 서 론

2. 연구 방법

3. 연구 결과

4. 결 론

키워드

Sentiment Analysis Text Mining Bankruptcy Prediction