Emotion classification through text with proposed hybrid learning emotion model

Inhalt:
Natural language processing (NLP) and artificial intelligence (AI) are important to enrich human-computer communication. The NLP is widely applied in various domains such as e-commerce, health, social media sentiment, etc. There has been an increasing need to deliver classes online since the COVID-19 pandemic, and various efforts and tools have been explored to improve students’ online studies. Students usually communicate with instructors and ask questions through text messages in online classes; Hence, NLP could be used to identify students’ emotions to improve the online learning experience. The current emotion classification works focused on the seven (7) universal emotions: anger, contempt, disgust, enjoyment, fear, sadness, and surprise. There is a lack of studies specializing in learning emotion classification. This research proposes a hybrid learning emotion model to predict students’ emotions through text messages. Emotions can affect the learner at different stages of the learning process. Understanding the student’s emotions is important because it will impact their attention, motivation, and self-regulated learning ability. The proposed hybrid learning emotion model is designed to classify four types of learning emotions: engagement, confusion, boredom, and hopefulness. In this research, the text messages from the student were collected based on the proposed hybrid learning emotion models, and the multinomial Naïve Bayes approach was used to predict the learning emotion.