Future-Generation Sentiment Analysis in Education: A Multiple modal, Bilingual, and Aritificial Intelligent Framework with Ethical Awareness

Abstract

Educational sentiment analysis is a new field that aims to understand and interpret students' emotions, attitudes, and engagement levels using data-driven methods. There is requirement for tailored and pliable learning environments which increases the ability to assess sentiment thoughts which has become vital for supporting student accomplishment and also to improve the teaching capabilities. This research suggests a thorough approach to improving sentiment analysis in education by using advanced Natural Language Processing (NLP) techniques, specifically transformer-based models like BERT and GPT, to better understand student feedback and communication.

Sentiment Analysis in educational sector is a novel field Which combines various teaching and study milieus. This research goes beyond just analysing text.  The main intention is to express the different sentiment views by combining the data from audio, video, facial expressions and socially written contents. The goal line is to generate a comprehensive model that imprisonments a broader range of sensitive signals. This study will also focus on developing multilingual models. This will help make the tools more useful for people from various language and cultural backgrounds, filling the inclusivity gap found in current sentiment analysis tools.