Student sentiment Analysis Using Classification With Feature Extraction Techniques

TL;DR

This paper explores machine learning methods like LR, SVM, NB, and DT to analyze student feedback sentiment in web-based learning, utilizing feature extraction techniques such as BoW and TF-IDF to improve classification accuracy.

Contribution

It introduces the application of multiple ML classifiers with feature extraction techniques for sentiment analysis of student feedback in web-based education.

Findings

Support Vector Machine achieved high accuracy in sentiment classification.

Feature extraction with TF-IDF improved model performance.

Naive Bayes provided quick and reliable sentiment predictions.

Abstract

Technical growths have empowered, numerous revolutions in the educational system by acquainting with technology into the classroom and by elevating the learning experience. Nowadays Web-based learning is getting much popularity. This paper describes the web-based learning and their effectiveness towards students. One of the prime factors in education or learning system is feedback; it is beneficial to learning if it must be used effectively. In this paper, we worked on how machine learning techniques like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) can be applied over Web-based learning, emphasis given on sentiment present in the feedback students. We also work on two types of Feature Extraction Technique (FETs) namely Count Vector (CVr) or Bag of Words) (BoW) and Term Frequency and Inverse Document Frequency (TF-IDF) Vector. In the research study, it is our goal for our proposed LR, SVM, NB, and DT models to classify the presence of Student Feedback Dataset (SFB) with improved accuracy with cleaned dataset and feature extraction techniques. The SFB is one of the significant concerns among the student sentimental analysis.