뉴스의 감성분석과 전문가 지식을 이용한 딥러닝 기반의 부동산 가격 예측
The prediction of real estate price based on deep learning using news sentiment and expert knowledge
신은경(부산대학교); 김은미(국민대학교 정보기술연구소); 홍태호(부산대학교)
22권 3호, 61~73쪽
초록
This study proposes a deep learning method for predicting real estate prices utilizing deep learning with real estate price time-series data, text data such as news, and experts' opinions. This study provides a method to integrate qualitative data into quantitative data when developing a deep learning model to predict time series. We employed house sales prices and economic data to build a prediction model for real estate prices from January 2006 to June 2021. In addition, we crawled 150,000 news information related to real estate and applied the TF-IDF method to identify the meaning of the news. We integrated the sentiment analysis and the experts' opinions into our proposed model. The experiment results show that our proposed method is valid in RMSE performance. It was also confirmed that LSTM was superior to short-term prediction in long-term prediction.
Abstract
This study proposes a deep learning method for predicting real estate prices utilizing deep learning with real estate price time-series data, text data such as news, and experts' opinions. This study provides a method to integrate qualitative data into quantitative data when developing a deep learning model to predict time series. We employed house sales prices and economic data to build a prediction model for real estate prices from January 2006 to June 2021. In addition, we crawled 150,000 news information related to real estate and applied the TF-IDF method to identify the meaning of the news. We integrated the sentiment analysis and the experts' opinions into our proposed model. The experiment results show that our proposed method is valid in RMSE performance. It was also confirmed that LSTM was superior to short-term prediction in long-term prediction.
- 발행기관:
- 한국인터넷전자상거래학회
- 분류:
- 경영학