Abstract
Emotion Classification based on text is a task with many applications which
has received growing interest in recent years. This paper presents a
preliminary study with the goal to help researchers and practitioners gain
insight into relatively new datasets as well as emotion classification in
general. We focus on three datasets that were recently presented in the related
literature, and we explore the performance of traditional as well as
state-of-the-art deep learning models in the presence of different
characteristics in the data. We also explore the use of data augmentation in
order to improve performance. Our experimental work shows that state-of-the-art
models such as RoBERTa perform the best for all cases. We also provide
observations and discussion that highlight the complexity of emotion
classification in these datasets and test out the applicability of the models
to actual social media posts we collected and labeled.