  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.
