Analysis of E-learner’s Opinion Using Automated Sentiment Analysis in E-learning and Comparison with Naive Bayes Classification, Random Forest and K-Nearest Neighbour Algorithms

E-learning becomes an online learning network that is more efficient and popular. Recognition of the views of the Commission’s e-learning users is also significant. E-learning is commonly referred to as instructional efforts transmitted to communicate information through computers in a non-traditional school atmosphere. Buyers cannot ask if their e-courses are relaxed. Sentiment mapping allows users using e-learning portals to be identified quickly. Romantic research uses natural information using language text analysis and computational linguistics to understand and extract contextual information from source materials. The goal is to determine the speaker’s attitude or the writer to the subject of the article’s general qualitative polarity. Automated identification of emotion allows recognizing user feelings on the webpages navigated by people. A topic or region in which they're interested for the e-learning method will use this information to understand emotional triggers of the individual and can be used to analyse the behaviour of the consumer every time. Feelings of the student against a course will serve reviews on online e-learning portals. In the proposed e-learning method, automatic emotion analysis allows evaluating the pages viewed on the customer’s e-portal. This allows the maker and instructor to realize where the learner works in particular fields for some physical labour or other feedback from the customer. Here, for the Bayesian predictive classification’s automatic emotion analysis, Naive Bayes classification, random forest and K-nearest neighbour algorithms are used here.