A Study on Artificial Intelligence-based Natural Language Processing for Online Shopping Services

As a result of the developments in the digital age, communication has become the primary route for modern business. Business discussions, profiles, seminars, transactions, and agreements were all conducted in person in the past, but today practically everything is done digitally. The goal of this work is to investigate how text-object connections made by artificial intelligence help NLP and AI better meet human requirements. Additionally, this work considers how modern applications like NLP and AI integrated with IoT services have altered online shopping environments. Online shopping service providers have created sophisticated NLP systems that take human demands and instantaneously satisfy them through text, phone conversations, smart records, and chatbots as communication takes center stage in the commercial environment. This work modeled the artificial intelligence-based natural language processing framework for online shopping services and evaluated using support vector machine (SVM) classification algorithm and a hybrid Naive Bayes - Hidden Markov model (NB-HMM) algorithm. The experimental analysis shows the better performance when the model use SVM for classification over NB-HMM with the average accuracy of 94.3% and F-score of 97.5%.