Descriptive Research on AI-based tools to aid personalized customer service
Case of ChatGPT
Keywords:
GPT, NLP, Sentiment Analysis, Named Entity Recognition, Intent Score, Customer ServiceAbstract
ChatGPT is a large language model with potential applications in natural language processing and conversational AI. We examine the usage of ChatGPT in customized customer support in this study. The objective would be to specifically look at how ChatGPT may improve consumer interactions with businesses. Data would be collected from customer service interactions in a number of businesses to perform this research. The data will then be analyzed using ChatGPT to detect customer behavior and preferences trends. A ChatGPT-based model would be created for personalized customer support according to the results of this research. The results presented in this study might have far-reaching consequences for firms aiming to improve their customer services. Businesses may be able to give more personalized and efficient service to their clients by utilizing the potential of ChatGPT, resulting in greater customer satisfaction and loyalty.
References
Aljanabi, M. (2023). ChatGPT: Future Directions and Open possibilities. Mesopotamian Journal of CyberSecurity, 2023, 16-17.
Chowdhury, N. A. (2023). Unlocking the Power of ChatGPT: An In-Depth Look at ChatAI's Business Model.
Shahriar, S., & Hayawi, K. (2023). Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations. arXiv preprint arXiv:2302.13817.
George, A. S., & George, A. H. (2023). A Review of ChatGPT AI's Impact on Several Business Sectors. Partners Universal International Innovation Journal, 1(1), 9-23.
Verma, M. (2023). Novel Study on AI-Based Chatbot (ChatGPT) Impacts on the Traditional Library Management.
Mattas, P. S. ChatGPT: A Study of AI Language Processing and its Implications. Journal homepage: www. ijrpr. com ISSN, 2582, 7421.
Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35.
Islam, I., & Islam, M. N. (2023). Opportunities and Challenges of ChatGPT in Academia: A Conceptual Analysis. Authorea Preprints.
Aydın, Ö., & Karaarslan, E. (2023). Is ChatGPT Leading Generative AI? What is Beyond Expectations?. What is Beyond Expectations.
Mitrović, S., Andreoletti, D., & Ayoub, O. (2023). ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text. arXiv preprint arXiv:2301.13852.
Sakirin, T., & Said, R. B. (2023). User preferences for ChatGPT-powered conversational interfaces versus traditional methods.
Mesopotamian Journal of Computer Science, 2023, 24-31.
Spaniol, M. J., & Rowland, N. J. (2023). AI‐assisted scenario generation for strategic planning. Futures & Foresight Science, e148.
Ariono, M. (2023). INA-RESPOND. INA-RESPOND, 13.
Bertsch, G. F., & Hagino, K. (2021). Configuration-interaction approach to nuclear fission. arXiv preprint arXiv:2102.07084.
Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu. 2020. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, edition.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
Roberts, A., & Raffel, C. (2020). Exploring transfer learning with t5: the text-to-text transfer transformer. Google AI Blog.
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