Abstract:
"
The human brain is not just a device for interpreting information; it is also a system in
which cognitive and affective processes are intimately connected. Any learning
process involves this interaction between emotion and cognition, with cognition
interacting with the learning material and emotion supplying the requisite mental
resources. The synthesis of learner's expressions in the teaching-learning context and
the effective managing of these emotions during the learning process in the first
approach, and emotion in the second approach which is one affective domain category
that requires further growth have been the subject of two separate approaches to
emotion research in teaching-learning contexts.
Evidential data shows that students do not switch feelings at will, but that there are
certain patterns; committed learners will feel cognitive disequilibrium and uncertainty
when confronted with challenges, and inability to regain stability will result in
dissatisfaction. Online learning environments that depend on text-based asynchronous
communication can intensify the degree of student anxiety, in comparison to face-to face teaching-learning situations, where issues may usually be addressed and
overcome more easily.
Sentiment analysis, described as ""the task of automatically deciding the valence or
polarity of a piece of text, is a promising solution. Sentiment analysis could be
described as a noninvasive, non-obtrusive, low-cost method, based on the design
requirements for emotion sensing systems. The aim of ELSent is to investigate the
ways in which sentiment analysis has been introduced in the educational domain; and
to discuss the methods that researchers have used in designing sentiment analysis
frameworks on educational datasets. ELSent is an improved classification model
which has given preprocessing, data imbalance and negation handling more attention
and investigate what is the most suitable classifier which gives human like accuracy
for an educational dataset. After doing a comparison between classifiers CatBoost
classifier outperformed by getting a 77% accuracy using educational dataset."
