Abstract
Huge amount of user-generated data are stored in the web. They are rich resources for analysis in many areas such as election result prediction, stock market analysis, and knowing customer satisfaction. Emotion classification can be used for these analysis. A graph-based approach was proposed by Argueta which can overcome the challenges of emotion classification. Even though it works for major Western languages, the other languages which are totally different from them are not tested. Also, the system returns fixed number of emotion results even though the number of emotions inside a tweet are not same. In this paper, we adopted the system to Japanese languages and proposed a statistical method to change the number of emotion results. Besides, the experiment to clarify the relation between the intensity of emotion and accuracy is conducted. Our results show that the system works also for Japanese but the accuracy differs when applying different segmentation methods are applied. By our method to change the number of emotion results, overall accuracy was improved. The result shows that when the intensity of emotion is strong, the accuracy will be higher.