Sentiment analysis is a method that allows us to analyze and understand the feelings and opinions expressed by people. This study focused on the sentiment exhibited by students in their feedback, reviews, and narratives to gain insights into their educational experiences. Our study aims to discern the emotional nuances and attitudes prevalent among students leveraging cutting-edge natural language processing (NLP) techniques through the analysis of textual data collected from various educational settings. This study also seeks to identify positive and negative sentiments. The research methodology entails gathering relevant data expressed on social media platforms such as the “X” platform, and then preprocessing was performed on the data to reduce noise, standardize formats, and prepare it for sentiment analysis. We applied machine learning techniques support vector machine (SVM), logistic regression (LR), K-nearest neighbors (KNN), and XGBoost (XGB) with term frequency-inverse document frequency (TF-IDF) and N-gram to extract the features and compare the results. Our results show that LR obtained the highest accuracy of 94%. The results of this study may help educational institutions in making appropriate decisions to improve and develop the quality of education.

sentiment analysis, universities, “X” platform, NLP, TF-IDF, N-gram.

Received: May 3, 2024; Accepted: June 1, 2024; Published: June 14, 2024

How to cite this article: Ghalih M. Al-Dwish and Amal N. Aljohani, Sentiment analysis of Saudi Arabian university students by using ML algorithms, Advances and Applications in Discrete Mathematics 41(5) (2024), 393-410. https://doi.org/10.17654/0974165824027

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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