Abstract
Recently, researches whose emphasizes Sentimental Analysis and Emotion Detections mainly go through large social data networks. People's posts behavior and their daily tweets in those networks portray emotions with valuable data, that can lead into product reviews, or emotion classification analysis. However, emotion detections on social network are sensitive, because they rely too much on the distribution of the emotion data. Some will be common because of trending topics, products, or life events, and some shows less and rare when it is unpopular or they are not trending. This results a gap in unbalanced distribution of emotions. Our study focuses on creating multiple functions for different objectives for weights learning to deal with unbalanced data. The design functions helps emotion detection systems to provide more detailed results, not just in their accuracy, but also precisions. Functions designed provides further handling improvements in different situations whose caused by unbalanced data.