ChatBot Integrated with Sentiment Anlaysis: Application to E-Learning

Online learning has become a popular choice for many schools and universities as well as for people with work or family obligations. However, many factors can lead the student to quit learning such as the lack of quality of the content, and the difficulty of use, the unavailability of support. These factors lead the student to become bored, demotivated, and drop out of the course. In this paper, an architecture of a chatbot integrated with sentiment analysis will be presented to determine the emotional state of the user based on the feedback collected from several e-learning platforms. In our case, a LSTM (Long Short-Term Memory) network is used with an accuracy of 91%. Integrating sentiment analysis with the chatbot in e-learning enables a more engaging and personalized learning experience for users. By understanding the emotional state of users, the chatbot can adapt its responses to provide more appropriate and effective support. In addition, by providing personalized feedback and support, the chatbot can help users identify and overcome obstacles they may encounter in their learning process.