EmoRecBiGRU: Emotion Recognition in Persian Tweets with a Transformer-based Model, Enhanced by Bidirectional GRU

Emotion recognition in text is a fundamental aspect of natural language understanding, with significant applications in various domains such as mental health monitoring, customer feedback analysis, content recommendation systems, and chatbots. In this paper, we present a hybrid model for predicting the presence of six emotions: anger, disgust, fear, sadness, happiness, and surprise in Persian text. We also predict the primary emotion in the given text, including these six emotions and the “other” category. Our approach involves the utilization of XLM-RoBERTa, a pre-trained transformer-based language model, and fine-tuning it on two diverse datasets: EmoPars and ArmanEmo. Central to our approach is incorporating a single Bidirectional Gated Recurrent Unit (BiGRU), placed before the final fully connected layer. This strategic integration empowers our model to capture contextual dependencies more effectively, resulting in an improved F-score after adding this BiGRU layer. This enhanced model achieved a ۲% improvement in the F-score metric on the ArmanEmo test set and a ۷% improvement in the F-score metric for predicting the presence of six emotions on the final test set of the ParsiAzma Emotion Recognition competition.

کلیدواژه ها:

Emotion Recognition (ER) ، Bidirectional Gated Recurrent Unit (BiGRU) ، Natural Language Processing (NLP) ، Large Language Model (LLM)