Nowadays chatbots has been an important trend on website, messaging application and some mobile apps. Chatbots are mainly used for providing information through user request analysis and returning responses. In the market, it has been proven to have enhanced in customer engagement, improved customer interaction and received positive reviews from customers with chatbot implemented on their website. In this information age, hiring customer service officer to answer all questions has been proven to be costly and new officer might give wrong information accidentally which might cause a lot of trouble to the organizations. Similarly, at School of Computer Science similarly has problems such as having many students continuously inquire the same questions and the school are short of staff member to answer all the questions simultaneously. Hence, a chatbot is proposed to answer all the questions that students may have in mind. This chatbot will be mainly focus on FAQ (frequently asked questions) from Master students to help clear their mind about the information of their Master courses in School of Computer Science. Ask Shela is proposed to answer questions provided by incoming Master students as much as possible. Ask Shela is implemented using NLP (Natural language processing) technique to understand the message and respond them appropriately. This chatbot (Ask Shela) is embedded on a webpage as a chat mechanism to answer questions posed by potential Master students. In this chatbot we are using intent classification by matching the intent with their examples of sentences using bat of words of the entities. The chatbot is implement using Python language and built by using neural network (relu) retified linear unit and optimizer of stochastic gradient descent. The reply is chosen by taking the highest confidence level among the intents. In this chatbot, a number of different questions is suggested for user to click and ask but user also able to write the questions themselves at the chatbox.

Here is the webpage for AskShela : https://unihwan.github.io/Ask-Shela-webpage/