Artificial Intelligence-based Sentiment Analysis Models in the Product and Service Industry: A State-of-the-art Review and Future Directions

• Published in: SN computer science Vol. 6; no. 7; p. 774
• Main Authors: Sethi, Hemant, Gupta, Rajeev
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.10.2025; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Business competition; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Customer feedback; Customer satisfaction; Customer services; Data Structures and Information Theory; Decision making; Emotions; Information Systems and Communication Service; Language; Libraries; Machine learning; Marketing; Natural language processing; Pattern Recognition and Graphics; Product differentiation; Product reviews; Real time; Review Article; Sentiment analysis; Service industries; Social networks; Software Engineering/Programming and Operating Systems; Trends; Vision; Working conditions; Multi-modal sentiments; Sentiment analysis; Natural language processing; Artificial intelligence
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-025-04317-2

Artificial intelligence-based sentiment analysis enables businesses to understand the meaningful insights of their customers to enhance the brand reputation and, in turn, can optimize the products and services offered to their customers. It provides accurate real-time insights, identifies growing trends, and facilitates data-driven decision-making. However, it also presents practical concerns such as bias, privacy, and contextual understanding issues. In the industrial sector, sentiment analysis is vital in helping establishments gain meaningful perceptions of text-based data like feedback on customer and employee sentiments and market trends. By leveraging sentiment analysis, industries can enhance product quality, streamline processes, foster a positive work environment, and develop effective marketing strategies, ultimately driving business growth and competitiveness. This paper took a more in-depth look at traditional AI-based sentiment analysis models, focusing on their uses and importance in business settings. It also talks about possible future research topics that are specific to the business world. The main objective of this study is to highlight and identify research gaps, identify trending challenges, and propose effective solutions to make informed decisions, enhance customer satisfaction, and improve products and services.