Linguistic Signatures for Enhanced Emotion Detection
This work addresses the need for interpretable signals in emotion detection for NLP applications, but it is incremental as it builds on existing transformer-based methods.
The study tackled the problem of understanding linguistic regularities in emotion detection by extracting emotion-specific linguistic signatures from 13 English datasets and incorporating them into transformer models, resulting in performance gains of up to +2.4 macro F1 on the GoEmotions benchmark.
Emotion detection is a central problem in NLP, with recent progress driven by transformer-based models trained on established datasets. However, little is known about the linguistic regularities that characterize how emotions are expressed across different corpora and labels. This study examines whether linguistic features can serve as reliable interpretable signals for emotion recognition in text. We extract emotion-specific linguistic signatures from 13 English datasets and evaluate how incorporating these features into transformer models impacts performance. Our RoBERTa-based models enriched with high level linguistic features achieve consistent performance gains of up to +2.4 macro F1 on the GoEmotions benchmark, showing that explicit lexical cues can complement neural representations and improve robustness in predicting emotion categories.