  In this paper, we present empirical analysis on basic and depression specific
multi-emotion mining in Tweets with the help of state of the art multi-label
classifiers. We choose our basic emotions from a hybrid emotion model
consisting of the common emotions from four highly regarded psychological
models of emotions. Moreover, we augment that emotion model with new emotion
categories because of their importance in the analysis of depression. Most of
those 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 Macro
F-measures and Micro 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.
