Advanced NLP Models for Technical University Information Chatbots: Development and Comparative Analysis

In order to achieve quality education as a defined one of the sustainable goals, it is necessary to provide information about the education system according to the stakeholders’ requirements. The process to obtain the information about university/institute is a critical stage in the academic journey of prospective students who are seeking information about the specific courses which makes that university/institute unique. This process begins with exploration to general information about universities through websites, rankings, and brochures from various sources. Most of the time, information available on different sources leads to discrepancies and influences student’s decisions. By addressing inquiries promptly and providing valuable information, universities can guide individuals in making informed choices about their academic future. To address this, the chatbot application is the most effective tool to be implemented and make it functional on university’s functional website. A chatbot is an artificially intelligent tool which can interact with humans and can mimic a conversation. This tool can be implemented using advanced Natural Language Processing (NLP) models to provide the pre-defined answers to the student’s queries. Chatbot is very helpful for query resolution during the counseling process of the institute as it will provide official/uniform information and can be accessed
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. Therefore, the aim of this research work was to implement a chatbot using various NLP models and compare them to identify best one. In this work, five chatbot models were implemented using neural networks, TF-IDF vectorization, sequential modeling and pattern matching. From the results, it was observed that neural network-related models had better accuracy than TF-IDF and pattern matching model, and sequential modeling is the most accurate model because it prevents over-fitting. Furthermore, a chatbot having any kind of optimizer can improve the result and it is most important that pattern matching, and semantic analysis should be the parts of a chatbot for real time scenarios.