Mohammad Anas, Anam Saiyeda, Shahab Saquib Sohail, Erik Cambria, Amir Hussain
{"title":"Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?","authors":"Mohammad Anas, Anam Saiyeda, Shahab Saquib Sohail, Erik Cambria, Amir Hussain","doi":"10.1109/mis.2024.3374582","DOIUrl":null,"url":null,"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.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mis.2024.3374582","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
IEEE Intelligent Systems serves users, managers, developers, researchers, and purchasers who are interested in intelligent systems and artificial intelligence, with particular emphasis on applications. Typically they are degreed professionals, with backgrounds in engineering, hard science, or business. The publication emphasizes current practice and experience, together with promising new ideas that are likely to be used in the near future. Sample topic areas for feature articles include knowledge-based systems, intelligent software agents, natural-language processing, technologies for knowledge management, machine learning, data mining, adaptive and intelligent robotics, knowledge-intensive processing on the Web, and social issues relevant to intelligent systems. Also encouraged are application features, covering practice at one or more companies or laboratories; full-length product stories (which require refereeing by at least three reviewers); tutorials; surveys; and case studies. Often issues are theme-based and collect articles around a contemporary topic under the auspices of a Guest Editor working with the EIC.