Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
This work addresses emotion mining for mental health analysis, specifically depression detection in social media, but is incremental as it applies existing methods to a new dataset with extended emotion categories.
The paper tackled multi-label emotion classification in tweets, including basic and depression-specific emotions, and found that a Deep Learning model performed best, achieving superior results in handling data imbalance and modeling semantic features.
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.