Fida Mohammad1,*, Mukhtaj Khan1, Safdar Nawaz Khan Marwat2, Naveed Jan3, Neelam Gohar4, Muhammad Bilal3, Amal Al-Rasheed5
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202
Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep… More >
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