Automated sentiment analysis of visually impaired students' audio feedback in virtual learning environments.

ABSTRACT: This research introduces an innovative intelligent model developed for predicting and analyzing sentiment responses regarding audio feedback from students with visual impairments in a virtual learning environment. Sentiment is divided into five types: high positive, positive, neutral, negative, and high negative. The model sources data from post-COVID-19 outbreak educational platforms (Microsoft Teams) and offers automated evaluation and visualization of audio feedback, which enhances students' performances. It also offers better insight into the sentiment scenarios of e-learning visually impaired students to educators. The sentiment responses from the assessment to point out deficiencies in computer literacy and forecast performance were pretty successful with the support vector machine (

SUBMITTER: Elbourhamy DM 

PROVIDER: S-EPMC11232573 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature