Sentiment analysis and emotion detection of post-COVID educational Tweets: Jordan case

Soc Netw Anal Min. 2023;13(1):39. doi: 10.1007/s13278-023-01041-8. Epub 2023 Mar 2.


Education evolved dramatically under Covid-19, and owing to the conditions, distant learning became mandatory. However, this has opened new realities to the educational business under the label of "Hybrid-Learning," where educational institutions are still using online learning in addition to face-to-face learning, which has changed people's lives and split their opinions and emotions. As a result, this study investigated the Jordanian community's perspectives and feelings on the transition from pure face-to-face education to blended education by examining related tweets in the post-COVID era. Specifically, using NLP Emotion detection and Sentiment Analysis approaches, as well as deep learning models. As a result of analyzing the collected tweets, 18.75% of studied Jordanian's community sample are dissatisfied (Anger and Hate), 21.25% are negative (Sad), 13% are Happy, and 24.50 percent are Neutral about it.

Keywords: Deep learning; Education; Emotion detection; Jordan; Natural language processing.