  Emotion recognition is one of the machine learning applications which can be
done using text, speech, or image data gathered from social media spaces.
Detecting emotion can help us in different fields, including opinion mining.
With the spread of social media, different platforms like Twitter have become
data sources, and the language used in these platforms is informal, making the
emotion detection task difficult. EmoPars and ArmanEmo are two new
human-labeled emotion datasets for the Persian language. These datasets,
especially EmoPars, are suffering from inequality between several samples
between two classes. In this paper, we evaluate EmoPars and compare them with
ArmanEmo. Throughout this analysis, we use data augmentation techniques, data
re-sampling, and class-weights with Transformer-based Pretrained Language
Models(PLMs) to handle the imbalance problem of these datasets. Moreover,
feature selection is used to enhance the models' performance by emphasizing the
text's specific features. In addition, we provide a new policy for selecting
data from EmoPars, which selects the high-confidence samples; as a result, the
model does not see samples that do not have specific emotion during training.
Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and
EmoPars, respectively, which are new state-of-the-art results in these
benchmarks.
