Document Type : Original Article

Authors

• Neha Singh
• Umesh Chandra Jaiswal

Madan Mohan Malaviya University of Technology, Gorakhpur, India

Abstract

Sentiment inquiry is used in a variety of sectors and has become one of the most popular subjects in academic exploration, with an expanding body of tasks. Maintaining a positive relationship between students requires academic input. Monitoring a student's progress is critical to their growth and helps instructors, parents, and guardians provide more support. Sentiment analysis is extensively used in a variety of fields, like business, social connections, and education. In an educational setting, this strategy allows students' feedback to be analysed, teachers' teaching performance to be monitored, and the learning experience to be improved. In the educational system, teacher assessment is critical to improving the learning experience in institutions. In this research, authors propose a novel ensemble machine learning technique for figuring out the best ways to help students study in order to boost their academic achievements. This research assesses the effectiveness of techniques using recall, precision, and f-measure. In order to compare the methods used in this study, the authors used a variety of machine learning approaches, including naive bayes, linear support vector machines, random forests, and logistic regression. When comparing several machine learning algorithms, the suggested ensemble technique produces the best results.

Keywords

• Education
• Sentiment Analysis
• Machine Learning
• Ensemble Model