The ancient Chinese board game, Go, with its simple rules yet highly complex strategies, requires players to encircle more territory than their opponent. However, owing to the rise in the capabilities of Go-playing software and a lack of Go instructors in Japan, there is a need for a software that actively assists human players in learning the high-level strategies required to win the game. This study focuses on generating a review for each consecutive player’s move. This paper studies how to generate an expression for each move based on the distribution of the stones on the board. To this task of generating the expression for each move in a game of Go, we apply a classifier learning technique.