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        <title>An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking</title>
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    <name type="personal">
        <namePart type="given">Yang</namePart>
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    <name type="personal">
        <namePart type="given">Deyu</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
        <namePart type="given">Yulan</namePart>
        <namePart type="family">He</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>Text might express or evoke multiple emotions with varying intensities. As such, it is crucial to predict and rank multiple relevant emotions by their intensities. Moreover, as emotions might be evoked by hidden topics, it is important to unveil and incorporate such topical information to understand how the emotions are evoked. We proposed a novel interpretable neural network approach for relevant emotion ranking. Specifically, motivated by transfer learning, the neural network is initialized to make the hidden layer approximate the behavior of topic models. Moreover, a novel error function is defined to optimize the whole neural network for relevant emotion ranking. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods. Moreover, the extracted emotion-associated topic words indeed represent emotion-evoking events and are in line with our common-sense knowledge.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1379</identifier>
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        <date>2018-oct-nov</date>
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            <start>3423</start>
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%0 Conference Proceedings
%T An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking
%A Yang, Yang
%A Zhou, Deyu
%A He, Yulan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yang-etal-2018-interpretable
%X Text might express or evoke multiple emotions with varying intensities. As such, it is crucial to predict and rank multiple relevant emotions by their intensities. Moreover, as emotions might be evoked by hidden topics, it is important to unveil and incorporate such topical information to understand how the emotions are evoked. We proposed a novel interpretable neural network approach for relevant emotion ranking. Specifically, motivated by transfer learning, the neural network is initialized to make the hidden layer approximate the behavior of topic models. Moreover, a novel error function is defined to optimize the whole neural network for relevant emotion ranking. Experimental results on three real-world corpora show that the proposed approach performs remarkably better than the state-of-the-art emotion detection approaches and multi-label learning methods. Moreover, the extracted emotion-associated topic words indeed represent emotion-evoking events and are in line with our common-sense knowledge.
%R 10.18653/v1/D18-1379
%U https://aclanthology.org/D18-1379
%U https://doi.org/10.18653/v1/D18-1379
%P 3423-3432

Markdown (Informal)

[An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking](https://aclanthology.org/D18-1379) (Yang et al., EMNLP 2018)

• An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking (Yang et al., EMNLP 2018)

ACL

• Yang Yang, Deyu Zhou, and Yulan He. 2018. An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3423–3432, Brussels, Belgium. Association for Computational Linguistics.