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Sentiment Analysis and its Application in the Education Sector: A Comprehensive Review

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Comprehensive review evaluates sentiment analysis methods in education, highlighting gaps and future potential.

Sentiment analysis shows promise in transforming educational practices, improving teaching strategies and learning outcomes.
Key methods include traditional machine learning models like Support Vector Machines and advanced techniques like LSTM and BERT.
Current challenges involve data quality and the lack of multilingual datasets, impacting model effectiveness and generalizability.
Future research should emphasize real-time tracking and adaptive learning systems to better cater to student needs.

Fahima et al. (2025) studied this question.

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