Natural Language Processing in Customer Service: A Systematic Review
This review synthesizes existing work on NLP in customer service, identifying trends and limitations, but it is incremental as it does not propose new methods or results.
This systematic review examined research from 2015 to 2022 on the use of natural language processing in customer service, finding that chatbots and question-answering systems are applied in 10 main fields, with Twitter as the second most common dataset and accuracy, precision, recall, and F1 as prevalent evaluation methods.
Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.