Detecting Fine-Grained Emotions from COVID-19 Tweets Using Transformer-Based Architecture
Rida Javed Kutty, Nazura Javed, Rahul Mallya · 2023
With the proliferation of social media, the task of identifying emotions in textual content has become increasingly relevant. Automating the task of detecting emotions from social media content has a wide range of applications today. In this work, we investigate the efficacy of pre-trained transformer models to detect fine-grained emotions from real-world tweets. We conduct experiments to fine-tune the DistilBERT and XLNet transformers to recognize nine emotions including neutral, anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Our DistilBERT and XLNet based models exhibit an F1-score of 0.6429 and 0.6679 respectively. The results are promising when compared with similar transformer-based models for fine-grained emotion recognition. We conclude from our study that fine-grained emotion detection from microblogs using transformers although promising is also challenging. The challenge is primarily on account of two reasons: a) difficulty in differentiating between intrinsically similar emotions and b) difficulty in building a large and balanced annotated corpus from real-world tweets.