Text might express or evoke multiple emotions with varying intensities . It is crucial to predict and rank multiple relevant emotions by their intensities… 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 . 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 . The extracted emotion-associated topic words indeed represent emotion-evoking events and are in line with our common-sense knowledge, according to our common sense of knowledge. Moreover, the extracted emotion

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Keywords : emotion - emotions - relevant - topical - sense -

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