Evaluation of Students' Opinions Regarding Higher Education Institutions Based on Sentiment Analysis by applying Machine Learning Approach.

 

The act of identifying and classifying the viewpoints of users included inside text documents into a range of various feelings, such as positive, negative, or neutral, is referred to as sentiment analysis. 

Utilizing an approach known as sentiment analysis allows for the unstructured language to be mined for information that is both organised and of value, and this may be done successfully. 

This data has the potential to be a useful source of information for decision-making support systems as well as for persons who are engaged in the process of decision-making on their own. In a wide variety of fields, including education, where student input is critical for assessing the effectiveness of educational institutions, sentiment analysis plays an important role in determining the efficacy of educational institutions. 

One of them is the field of education. There is a possibility that we will be able to give a way of sentiment analysis that is based on machine learning and focuses on the manner in which students assess institutions of higher education. 

We can perform an analysis of a corpus that has around n reviews written by students and published on a specific platform by using normal text representation methods and machine learning classifiers. This analysis is carried out on a particular platform. In the experimental study, three traditional text representation schemes (i.e., term-presence, term-frequency, and the TF-IDF scheme), three N-gram models (1-gram, 2-gram, and 3-gram), and four distinct classifiers were taken into consideration. The term-presence scheme was shown to be the most effective overall (i.e., support vector machines, Naive Bayes, logistic regression, and the random forest algorithm). It may be done with respect to cited heading.............................