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

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

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 2% improvement in the F-score metric on the ArmanEmo test set and a 7% improvement in the F-score metric for predicting the presence of six emotions on the final test set of the ParsiAzma Emotion Recognition competition.

Language:
English
Published:
International Journal Information and Communication Technology Research, Volume:16 Issue: 3, Summer 2024
Pages:
35 to 44
https://www.magiran.com/p2784365  
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