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• W3208019714 abstract "In this article, we propose a novel approach to bootstrap a general seed emotion lexicon with words found in a domain-specific corpus. The approach divulges the contextual similarity between two words in the corpus via lexical-, dictionary-, and topic-based features, thus revealing the emotion labels of domain-specific words. As unfolding the recursive structure of language is an irreducible component of how humans understand a sentence, in this study, a propagation mechanism is designed that takes advantage of a shallow parser to derive the emotions associated with the words and their parent phrases. This mechanism pushes beyond the limits of most word co-occurrence approaches and facilitates the multilabel emotion tagging of a sentence in a manner reflecting human cognition. Evaluations on two benchmark corpora support the validity of the propagation mechanism. Further evaluation of a financial corpus indicates that our system outperforms the traditional bag-of-words approach. Our approach provides better modeling of compositional emotions by considering the emotion-bearing words, shifters, intensifiers, and overall sentence structure." @default.
• W3208019714 created "2021-11-08" @default.
• W3208019714 creator A5015584181 @default.
• W3208019714 date "2022-08-01" @default.
• W3208019714 modified "2023-10-07" @default.
• W3208019714 title "Multilabel Emotion Tagging for Domain-Specific Texts" @default.
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• W3208019714 doi "https://doi.org/10.1109/tcss.2021.3121909" @default.
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