Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?
Mohammad Anas, Anam Saiyeda, Shahab Saquib Sohail, Erik Cambria, Amir Hussain
Abstract
Recent advances in the context of deep learning have led to the development of generative artificial intelligence (AI) models which have shown remarkable performance in complex language understanding tasks. This study proposes an evaluation of traditional deep learning algorithms and generative AI models for sentiment analysis. Experimental results show that RoBERTa outperforms all models, including ChatGPT and Bard, suggesting that generative AI models are not yet able to capture the nuances and subtleties of sentiment in text. We provide valuable insights into the strengths and weaknesses of different models for sentiment analysis and offer guidance for researchers and practitioners in selecting suitable models for their tasks.