Abstract
This paper improves a method which predicts whether evaluation objects such as companies and products are to be attractive in near future. The attractiveness is evaluated by trend rules. The trend rules represent relationships among evaluation objects, keywords, and numerical changes related to the evaluation objects. They are inductively acquired from text sequential data and numerical sequential data. The method assigns evaluation objects to the text sequential data by activating a topic dictionary. The dictionary describes keywords representing the numerical change. It can expand the amount of the training data. It is anticipated that the expansion leads to the acquisition of more valid trend rules. This paper applies the method to a task which predicts attractive stock brands based on both news headlines and stock price sequences. It shows that the method can improve the detection performance of evaluation objects through numerical experiments.
Highlights
Various kinds of sequential data are and cheaply collected from real world and virtual world
Maximum value of class counters assigned to an evaluation object ≥ minimum number of transactions
The proposed method cannot identify original evaluation objects directly described in news headlines with the ones based on the topic dictionary
Summary
Various kinds of sequential data are and cheaply collected from real world and virtual world. It is anticipated that the data includes the knowledge that brings smart life to us. Many researches aggressively tackle on the knowledge discovery task from the data [1,2,3,4,5]. The knowledge discovery task depends on features of the data and types of the knowledge. It is impossible to deal with all features and all types by only a method. It is indispensable to develop a discovery method reflecting target features and types
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