Improving user experience by ANN prediction and NLP chatbot

Abstract

As communication between service providers and users increases daily due to the large supply and demand for services, the application of artificial intelligence in business domains can greatly facilitate and improve characteristics such as the speed and efficiency of communication through the implementation of NLP chatbots presented in this work. This work aims to investigate the consequences of NLP chatbot implementation and the reliability of ANN prediction in terms of user experience, and to determine whether the developed NLP chatbot application matches the ingenuity in understanding communication in terms of algorithm reliability. It also aims to verify whether differences in the improvement of the user experience by NLP chatbots can be detected through a comparative analysis and what impact the speed of NLP chatbots has on the user experience compared to a chatbot without NLP function.

The programming of the application and the simulation were done with the Python programming language in the PyCharm integrated development environment using the artificial neural network model and libraries such as PyTorch, NLTK and Tkinter. The creation of the experimental part consisted of the creation of an NLP chatbot application, the creation of a simple chatbot represented by a simulation display, and the creation of a comparative analysis between an NLP chatbot and a rule-based chatbot.

The proposed model, primarily the concept itself, can also be used for other domains that could significantly benefit from the implementation of NLP chatbots, and not only for the domains highlighted in this paper.