Affective computing is a very important issue. An increasing amount of research focused on representing affective states as continuous numerical values on multiple dimensions. Such as the emotional space, which is about the valence and arousal. Due to the affective dimension representation can be useful to sentiment analysis, building dimensional sentiment resources with valence-arousal ratings are very important. Therefore, this study proposes a method to automatically obtain the valence-arousal ratings of affective words. Experiment results using the evaluation metrics to get the error rates about the mean absolute error and pearson correlation coefficient.