Unsupervised graph-based patterns extraction for emotion classification

Traditional classifiers require extracting high dimensional feature representations, which become computationally expensive to process and can misrepresent or deteriorate the accuracy of a classifier. By utilizing a more representative list of extracted patterns, we can improve the precision and recall of a classification task. In this paper, we propose an unsupervised graph-based approach for bootstrapping Twitter-specific emotion-bearing patterns. Due to its novel bootstrapping process, the full system is also adaptable to different domains and classification problems. Furthermore, we explore how emotion-bearing patterns can help boost an emotion classification task. The experimented results demonstrate that the extracted patterns are effective in identifying emotions for English, Spanish and French Twitter streams.