Sentiment topic emotion model on students feedback for educational benefits and practices

摘要

The advances in social platform services have gained momentum and expedited the sharing of emotions through blogs, micro blogs/tweets, review messages, online chats and so forth. There are two categories of emotions evoked, one in the writer's perspective and the other in the reader's perspective. The proposed system performs sentiment and emotion analysis on the student feedback to estimate the performance of the teacher as well as to identify the student's satisfaction on the course. The sentiment topic model generates the latent topics from the social emotion directly and it can be used to detect the sarcasm which is an irony word used to convey implicit meaning within the message which is very hard to recognise. The model is used to categorise emotions and it also generates emotion lexicons. The sentiment topic emotion model designed is used for predicting the emotion topic which subsequently yields the satisfaction and dissatisfaction of the student towards the teacher's performance for the course. The social emotion lexicon developed can further determine meaningful latent topics focusing on the emotions.