SNS와 뉴스기사의 감성분석과 기계학습을 이용한 주가예측 모형 비교 연구

• 영문명: A Comparative Study between Stock Price Prediction Models Using Sentiment Analysis and Machine Learning Based on SNS and News Articles
• 발행기관: 한국IT서비스학회
• 저자명: 김동영(Dong young Kim) 박제원(Jea won Park) 최재현(Jae hyun Choi)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제13권 제3호, 211~233쪽, 전체 23쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2014.09.30

영문 초록

Because people's interest of the stock market has been increased with the development of economy, a lot of studies have been going to predict fluctuation of stock prices. Latterly many studies have been made using scientific and technological method among the various forecasting method, and also data using for study are becoming diverse. So, in this paper we propose stock prices prediction models using sentiment analysis and machine learning based on news articles and SNS data to improve the accuracy of prediction of stock prices. Stock prices prediction models that we propose are generated through the four-step process that contain data collection, sentiment dictionary construction, sentiment analysis, and machine learning. The data have been collected to target newspapers related to economy in the case of news article and to target twitter in the case of SNS data. Sentiment dictionary was built using news articles among the collected data, and we utilize it to process sentiment analysis. In machine learning phase, we generate prediction models using various techniques of classification and the data that was made through sentiment analysis. After generating prediction models, we conducted 10-fold cross-validation to measure the performance of they. The experimental result showed that accuracy is over 80% in a number of ways and F1 score is closer to 0.8. The result can be seen as significantly enhanced result compared with conventional researches utilizing opinion mining or data mining techniques.

목차

Abstract

1. 서론

2. 관련 연구

3. 주가예측 모형

4. 실험 및 결과

5. 결론

References

키워드

Data Mining Stock Price Prediction Sentiment Analysis SNS Big Data Machine Learning