A Multicriteria Framework for Assessing Sentiment Analysis in Social and Digital Learning: Software Review
Christos Troussas, Akrivi Krouska, Maria K. Virvou · 2018
Sentiment Analysis has become a powerful tool that supports the field of e-Iearning variously. Learners can unintentionally provide priceless information to the instructors concerning their emotional state. Educational software can then use this information to adapt the learning process to the emotional needs of learners. Similarly, instructors can then use this information in order render the learning experience more attractive. For instance, when sentiment analysis detects an emotional state of a student who has no interest in learning a specific subject, it can then motivate him/her not to quit learning. This paper presents a novel multicriteria framework for assessing sentiment analysis both in social and digital learning environments. Using this framework, a comparative analysis of sentiment analysis software is conducted. Its results demonstrate that there is scope for improvement in the field of sentiment analysis is social and digital learning.