Sentiment analysis has become an essential tool across various domains, including the education sector, where its application remains relatively underexplored. This study presents a comprehensive review of sentiment analysis in educational contexts, evaluating traditional machine learning models such as Support Vector Machines (SVM) and Naive Bayes, as well as advanced deep learning approaches like Long Short-Term Memory (LSTM) networks and transformer-based models such as BERT. These methods have been applied to analyze student feedback, predict academic performance, and improve teaching strategies. Model performance is assessed using accuracy, precision, recall, and F1-score, though challenges persist related to data quality, annotation consistency, and the contextual complexity of student language. The review identifies significant research gaps, particularly the lack of multilingual and culturally diverse datasets, which limits the generalizability of current models. Furthermore, real-time sentiment tracking and adaptive feedback systems remain underdeveloped. To address these issues, the study proposes the integration of AI-enabled adaptive learning environments capable of dynamically responding to learners’ emotional and cognitive states, thus enhancing personalization and educational effectiveness. Overall, sentiment analysis holds significant promise for transforming educational practices, provided future research focuses on inclusivity, contextual awareness, and scalability.
Fahima et al. (Thu,) studied this question.
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