Adopting Text Similarity Methods and Cloud Computing to Build a College Chatbot Model
AbstractA chatbot is a computer program which is designed to interact with users and answer questions. Nowadays, chatbots are one of the most common systems that are used in many fields and by different companies to achieve different tasks. Cloud computing is gaining increasing interest. A myriad of fields and applications have been developed based on cloud computing.
In this paper, a college chatbot was developed and implemented to assist students to interact with their college and ask questions related to faculty, activities, exams, admission, amongst other tasks. Text similarity algorithms were adopted to achieve the proposed system. More specifically, cosine similarity and jaccard similarity algorithms were used to find the closest question in the dataset. Firebase real-time database, which is one of the Google cloud services, was used as a connector channel between users and the chatbot server.
Experiments were conducted to evaluate the performance of cosine similarity and jaccard similarity methods, and to compare the results of both. In addition, real-time database was also evaluated as a chatbot connecter channel.
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