Basic and depression specific emotion identification in Tweets: multi-label classification experiments
- Nawshad Farruque
- Chenyang Huang
- Osmar R. Zaiane, University of Alberta (Database)
- Randy Goebel
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 multilabel 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.
Citation
N. Farruque, C. Huang, O. Zaiane, R. Goebel. "Basic and depression specific emotion identification in Tweets: multi-label classification experiments". International Conference on Intelligent Text Processing and Computational Linguistics (CICLing), La Rochelle, France, April 2019.| Keywords: | |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Farruque+al:CICLing19,
author = {Nawshad Farruque and Chenyang Huang and Osmar R. Zaiane and Randy
Goebel},
title = {Basic and depression specific emotion identification in Tweets:
multi-label classification experiments},
booktitle = {International Conference on Intelligent Text Processing and
Computational Linguistics (CICLing)},
year = 2019,
}Last Updated: November 03, 2019Submitted by Sabina P
