Application of Naive Bayes Algorithm with TF-IDF Weighting and Lexicon Approach for Sentiment Analysis of Student Opinions on Campus Facilities

Abstract

This study aims to analyze student sentiment towards campus facilities at Nurul Jadid University using the NaiveBayes method. Data was collected through questionnaires and processed using Visual Studio Code software. The process stages included manual sentiment classification, data preprocessing, lexicon classification, TF-IDF weighting, and classification using the Naive Bayes method. The results of each step are presented in tables and graphs. The system was also implemented by creating a web application that allows users to enter new text/opinions and obtain sentiment classification results. The results of testing manual sentiment classification and lexicon with the Naive Bayes algorithm showed different levels of accuracy, with manual sentiment classification having an accuracy value of 75% and lexicon sentiment classification having an accuracy value of 85%. In conclusion, the lexicon approach with the Naive Bayes algorithm is superior to the manual approach because it is more objective, consistent, efficient, and easy to develop and is suitable for analyzing student opinions on campus facilities, thus providing material for consideration in campus policy.