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.
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