Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms

Chatbots actively help the human user through digital conversation through NLP based on artificial intelligence (AI). It can be pre-trained to understand the user's queries and produce an immediate response in NLP. The user input of the chatbot is any format like voice, text, sentiments, etc. Many research works have been implemented. The issues of existing works are that during the digital conversation, it does not accurately identify the user's requirement, it may go irrelevant to the user's query, and also, primarily, it is voice-based and faces hitches in the analysis of the user's intention, unable to track the context in long-conversation. Therefore, for understanding the context, sentimental calculations are essential. This paper proposed to make the immediate response of users in the chatbot by using Bi-directional Recurrent Neural Network with a Fuzzy Naïve Bayes classifier (BRNN-FNB). This paper aims to build chatbot models with AI-based sentimental analysis, which helps humans to perform accurate interactions. The working concept of chatbots is based on two forms of artificial intelligence domains: machine learning and natural language processing. It may be used in many applications like digital marketing, education, and online forums. The accuracy rate of the proposed work BRNN-FNB got 93% using the Seq-to-Seq technique. And also, the accuracy rate of the proposed work BRNN-FNB without using Seq-to-Seq got 92%.