Ranking of Evaluation Targets Based on Complex Sequential Data

This paper proposes a method that arranges evaluation targets based on their complex sequential data. The data is composed of numerical one and text one. This method focuses on both change ratios of the numerical sequential data and the occupation ratios of the text sequential data. It generates a ranking model of the evaluation targets. The model can extract important evaluation targets. This paper applies the method to the data composed of stock price information and news articles. The former one corresponds to the numerical sequential data and the latter one corresponds to the text sequential data. Lastly, this paper compares the method with a method based on random selection and shows the effect of the proposed method.