Basic and Depression Specific Emotions Identification in Tweets: Multi-label Classification Experiments

Abstract

We present an empirical analysis of basic and depression specific multi-emotion mining in Tweets, using state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the commonly identified emotions from four highly regarded psychological models. Moreover, we augment that emotion model with new emotion categories arising from their importance in the analysis of depression. Most of these additional emotions have not been used in previous emotion mining research. Our experimental analyses show that a cost sensitive RankSVM algorithm and a Deep Learning model are both robust, measured by both Micro F-Measures and Macro F-Measures. This suggests that these algorithms are superior in addressing the widely known data imbalance problem in multi-label learning. Moreover, our application of Deep Learning performs the best, giving it an edge in modeling deep semantic features of our extended emotional categories.