Abstract
Text might contain or invoke multiple emotions
with varying intensities. As such, emotion
detection, to predict multiple emotions associated
with a given text, can be cast into a
multi-label classification problem. We would
like to go one step further so that a ranked
list of relevant emotions are generated where
top ranked emotions are more intensely associated
with text compared to lower ranked
emotions, whereas the rankings of irrelevant
emotions are not important. A novel framework
of relevant emotion ranking is proposed
to tackle the problem. In the framework, the
objective loss function is designed elaborately
so that both emotion prediction and rankings
of only relevant emotions can be achieved.
Moreover, we observe that some emotions cooccur
more often while other emotions rarely
co-exist. Such information is incorporated into
the framework as constraints to improve the
accuracy of emotion detection. Experimental
results on two real-world corpora show that
the proposed framework can effectively deal
with emotion detection and performs remarkably
better than the state-of-the-art emotion
detection approaches and multi-label learning
methods.
with varying intensities. As such, emotion
detection, to predict multiple emotions associated
with a given text, can be cast into a
multi-label classification problem. We would
like to go one step further so that a ranked
list of relevant emotions are generated where
top ranked emotions are more intensely associated
with text compared to lower ranked
emotions, whereas the rankings of irrelevant
emotions are not important. A novel framework
of relevant emotion ranking is proposed
to tackle the problem. In the framework, the
objective loss function is designed elaborately
so that both emotion prediction and rankings
of only relevant emotions can be achieved.
Moreover, we observe that some emotions cooccur
more often while other emotions rarely
co-exist. Such information is incorporated into
the framework as constraints to improve the
accuracy of emotion detection. Experimental
results on two real-world corpora show that
the proposed framework can effectively deal
with emotion detection and performs remarkably
better than the state-of-the-art emotion
detection approaches and multi-label learning
methods.
| Original language | English |
|---|---|
| Title of host publication | The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) |
| Publisher | Association for Computational Linguistics |
| Pages | 561-571 |
| DOIs | |
| Publication status | Published - 1 Jun 2018 |
| Event | The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 http://naacl2018.org/ |
Conference
| Conference | The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL) |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 1/06/18 → 6/06/18 |
| Internet address |
Bibliographical note
© 2018 Association for Computational LinguisticsFunding: National Key R&D Program of China (No. 2017YFB1002801), the National Natural Science Foundation of China (61772132), the Natural Science Foundation of Jiangsu Province of China (BK20161430) and Innovate UK (103652).