Relevant Emotion Ranking from Text Constraint with Emotion Relationships


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: © 2018 Association for Computational Linguistics Funding: 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).
Event Title: The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL)
Event Type: Other
Event Dates: 2018-06-01 - 2018-06-06
Last Modified: 13 Jun 2024 07:44
Date Deposited: 11 May 2018 12:45
PURE Output Type: Conference contribution
Published Date: 2018-06-01
Accepted Date: 2018-02-14
Authors: Zhou, Deyu
Yang, Yang
He, Yulan (ORCID Profile 0000-0003-3948-5845)



Version: Accepted Version

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