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

TL;DR

This study conducts empirical analysis of multi-label emotion classification in tweets, focusing on basic and depression-specific emotions, using advanced classifiers like RankSVM and Deep Learning to handle data imbalance and improve accuracy.

Contribution

It introduces an extended emotion model including depression-related emotions and compares the effectiveness of RankSVM and Deep Learning classifiers in this context.

Findings

Deep Learning outperforms other models in emotion classification.

Cost-sensitive RankSVM effectively handles data imbalance.

Extended emotion categories improve depression analysis.

Abstract

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.