Description on the Implementation Method for Word Tendency Using Decision Tree

In every text, words have various frequencies and keywords have strong relationship with the subjects of their texts. Word frequencies change due to time-series variation over given periods of time. An early method estimated stability classes that indicate word popularity due to time-series variation based on frequency changes in text data over given periods using a decision tree. The estimation precision of the decision tree decreases when there is scattering of data number among classes. This paper suggests a new way to use a Random Sampling Method and proposes a new Data Copying Method to improve the estimation precision of decision tree. By using this new Data Copying Method, F-measures have improved: Increasing Class 9%; Relatively Constant Class 9%; Decreasing Class 18%. With increasing creation of electronic texts, there will be increased indexing of word popularity based on time-series variation. The method presented in this paper should be particularly valuable in estimating word popularity in searches of similar text. Future work could focus on fields other than sports or professional baseball as used in this paper.