Linguistic Signatures for Enhanced Emotion Detection
arXiv:2603.20222v1 Announce Type: new Abstract: 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.
arXiv:2603.20222v1 Announce Type: new Abstract: 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.
Executive Summary
This study investigates the potential of linguistic features as interpretable signals for emotion recognition in text. By extracting emotion-specific linguistic signatures from 13 English datasets, the researchers demonstrate that incorporating these features into transformer models can achieve consistent performance gains of up to +2.4 macro F1 on the GoEmotions benchmark. The findings suggest that explicit lexical cues can complement neural representations and improve robustness in predicting emotion categories. The study's results have significant implications for the development of more accurate and interpretable emotion detection systems, which can be applied in various fields, including psychology, marketing, and customer service. However, the study's reliance on existing datasets and the lack of exploration of linguistic features in other languages are notable limitations.
Key Points
- ▸ Linguistic features can serve as reliable interpretable signals for emotion recognition in text.
- ▸ Incorporating linguistic features into transformer models can achieve consistent performance gains.
- ▸ Emotion-specific linguistic signatures can be extracted from various English datasets.
Merits
Improved Emotion Detection Accuracy
The study demonstrates significant performance gains in emotion detection tasks, indicating the potential of linguistic features to enhance the accuracy of emotion recognition systems.
Interpretable Results
The use of linguistic features provides an interpretable explanation for the predictions made by the emotion detection system, enabling researchers and practitioners to better understand the underlying mechanisms.
Demerits
Limited Generalizability
The study's findings are based on a limited set of English datasets, which may not generalize to other languages or text styles.
Overreliance on Existing Datasets
The study relies heavily on existing emotion detection datasets, which may not capture the full range of linguistic features and emotion expressions.
Expert Commentary
This study makes a significant contribution to the field of emotion detection by demonstrating the potential of linguistic features to enhance the accuracy and interpretability of emotion recognition systems. However, the study's reliance on existing datasets and the lack of exploration of linguistic features in other languages are notable limitations. Future studies should aim to address these limitations by investigating linguistic features in multilingual settings and exploring the application of linguistic analysis to sentiment analysis tasks. Additionally, the study's findings have significant implications for the development of more accurate and interpretable emotion detection systems, which can be applied in various fields.
Recommendations
- ✓ Future studies should investigate the application of linguistic features in multilingual settings to improve the generalizability of emotion detection systems.
- ✓ Researchers should explore the use of linguistic analysis for sentiment analysis tasks to better understand the underlying linguistic mechanisms.
Sources
Original: arXiv - cs.CL
