Abstract
Emotion Detection for Unbalanced Indonesian Tweets ABSTRACT Research concerning Twitter mining becomes an interesting research topic in recent years. Emotion detection is one of research area which uses microblog, such as Twitter, to discover emotions from textual data. Recently, a novel technique based on graph-based was proposed to extract patterns that bear emotion. The system has been achieved a good performance in different languages. By adopting the system, we are motivated to enhance the accuracy of emotion detection for Indonesian language which consists of eight emotions, i.e. joy (senang), sad (sedih), fear (takut), surprise (terkejut), disgust (jijik), anticipation (antisipasi), trust (percaya), dan anger (marah). The data distribution among the emotions is really unbalanced which make the low precision of system for Indonesian language. In this study, we proposed an adjusting pattern weight to address unbalanced data problem for Indonesian language. The experiment results show that the proposed approach can improve the precision for unbalanced Indonesian data.