Multilabel Emotion Tagging for Domain-Specific Texts

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.