Analysis of Faculty Evaluation Through Student Feedback Using Sentiment Modeling

Faculty evaluation is vital in evaluating the teaching effectiveness of the faculty members. This study aimed to analyze the faculty evaluation through student's feedback using sentiment modeling, Specifically, the study sought to gather and preprocess the purpose of the recent student feedbacks, to train sentiment analysis models for the faculty evaluation feedbacks, and to evaluate the performance of the models in terms of accuracy and Fl score. The researchers used the quantitative method which the dataset of students' feedback were transformed into numerical form, likewise, applied research design was utilized in the study for the sentiment analysis. The logistic regression, Naïve Bayes, and SVM model classifier were used to test the dataset and confusion matrix to determine the accuracy and F1 score. Based on the sentiment analysis, the SVM model classifier performed accurately other than the other classifiers.

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