Using Domain Knowledge to Enhance Deep Learning for Emotional Intelligence
Emotion identification provides information to managers and more granular emotions provide more specific information. Whereas most existing emotion classifiers focus on a coarse set of emotions (e.g., sadness, joy), we focus on a larger set of 24 granular emotions (e.g., disappointment, neglect, sympathy). Granular classification is challenging because it aims to distinguish subordinate-level categories, which often have small inter-class variation but large intra-class variation. We propose a hierarchical classification architecture for identifying granular emotions in unstructured text data. The proposed classifier takes advantage of a semantic network of emotions from psychology which maps out how individuals categorize emotions. In the first stage of classification, input text is classified as being one or more coarse emotions. In the second stage, a more granular emotion is identified based on the coarse classification of the previous stage. Using self-labeled Twitter data, we find that our proposed hierarchical classifier outperforms a single-stage flat classifier in terms of F1 by increasing recall at the cost of precision. In addition, the hierarchical structure increases the explainability of the model by enabling interpretation at multiple levels, providing additional insight to end users.
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